Our statistical practice is regulated by the Office for Statistics Regulation (OSR). The OSR sets the standards of trustworthiness, quality and value in the Code of Practice for Statistics that all producers of official statistics should adhere to.

Provisional monthly uptake data for seasonal influenza and COVID-19 vaccines for frontline HCWs working in trusts, independent sector healthcare providers (ISHCPs), and GP practices in England.

Data is presented at national, NHS regional and individual trust levels.

View the pre-release access list for these reports.

The Frontline Worker Training market is experiencing robust growth, driven by the increasing need for upskilling and reskilling a large and diverse workforce. This market is projected to reach a significant size, with a Compound Annual Growth Rate (CAGR) indicating substantial expansion over the forecast period. While precise figures for market size and CAGR aren't provided, a reasonable estimate based on similar industry trends and the listed companies' presence suggests a substantial market value, potentially in the billions of dollars by 2033. The substantial investment by numerous companies (PTC, Beekeeper, Microsoft, etc.) in this space further underscores its significant potential. Key drivers include the rising adoption of digital learning platforms, regulatory compliance mandates necessitating employee training, and the growing emphasis on improving operational efficiency and employee retention. Emerging trends within the market include the rise of microlearning, personalized learning experiences tailored to individual needs and roles, and the integration of augmented reality (AR) and virtual reality (VR) technologies for immersive and engaging training. The increased demand for mobile-first learning solutions caters to the always-on nature of frontline work. Restraints may include the high initial investment required for implementing new technologies, challenges in engaging and retaining frontline workers in training programs, and concerns about data security and privacy. This dynamic market landscape is characterized by competition among established players and the emergence of innovative solutions. The presence of numerous regional companies in the list of companies suggests a global reach with opportunities for growth in diverse geographical areas. Successfully navigating these challenges will be crucial for companies to capitalize on the immense opportunities within the frontline worker training market.

BackgroundHealthcare workers (HCWs) from COVID-19 hotspots worldwide have reported poor mental health outcomes since the pandemic's beginning. The virulence of the initial COVID-19 surge in Spain and the urgency for rapid evidence constrained early studies in their capacity to inform mental health programs accurately. Here, we used a qualitative research design to describe relevant mental health problems among frontline HCWs and explore their association with determinants and consequences and their implications for the design and implementation of mental health programs.Materials and methodsFollowing the Programme Design, Implementation, Monitoring, and Evaluation (DIME) protocol, we used a two-step qualitative research design to interview frontline HCWs, mental health experts, administrators, and service planners in Spain. We used Free List (FL) interviews to identify problems experienced by frontline HCWs and Key informant (KI) interviews to describe them and explore their determinants and consequences, as well as the strategies considered useful to overcome these problems. We used a thematic analysis approach to analyze the interview outputs and framed our results into a five-level social-ecological model (intrapersonal, interpersonal, organizational, community, and public health).ResultsWe recruited 75 FL and 22 KI interviewees, roughly balanced in age and gender. We detected 56 themes during the FL interviews and explored the following themes in the KI interviews: fear of infection, psychological distress, stress, moral distress, and interpersonal conflicts among coworkers. We found that interviewees reported perceived causes and consequences across problems at all levels (intrapersonal to public health). Although several mental health strategies were implemented (especially at an intrapersonal and interpersonal level), most mental health needs remained unmet, especially at the organizational, community, and public policy levels.ConclusionsIn keeping with available quantitative evidence, our findings show that mental health problems are still relevant for frontline HCWs 1 year after the COVID-19 pandemic and that many reported causes of these problems are modifiable. Based on this, we offer specific recommendations to design and implement mental health strategies and recommend using transdiagnostic, low-intensity, scalable psychological interventions contextually adapted and tailored for HCWs.

This dataset contains the tweet ids of 63,707 tweets posted by the Twitter accounts of the International Brotherhood of Teamsters as well as affiliated locals. The purpose of the collection was to document the experience of Teamsters members as front line workers, as well as the union's role in supporting its members, during the COVID-19 pandemic. This data was collected as part of the IBT COVID-19 Response Collection at George Washington University Libraries Special Collections. Tweets were collected between May 8, 2020 and Feb. 23, 2022, from the GET statuses/user_timeline method of the Twitter v1 REST API using Social Feed Manager. Collecting from the Twitter timelines harvests some tweets that were posted prior to the collection date, so this collection includes tweets as far back as 2015 in some cases. The teamsters_locals_ids.txt file contains the IDs of the tweets collected. The list of screen names included in the collection can be found in the teamsters_locals_filter_README.txt file. The teamsters_locals_filter_README.txt file also contains additional documentation on how the tweets were collected, including the dates and times that screen names were added to collecting, and when collections occurred. The GET statuses/lookup method supports retrieving the complete tweet for a tweet ID (known as hydrating). Tools such as Twarc or Hydrator can be used to hydrate tweets. Per Twitter’s Developer Policy, tweet ids may be publicly shared for academic purposes; tweets may not. Questions about this dataset can be sent to sfm@gwu.edu. George Washington University researchers should contact us for access to the tweets.

Background: The outbreak of novel coronavirus disease 2019 (COVID-19) has caused public panic and psychological health problems, especially in medical staff. We aimed to investigate the psychological effect of the COVID-19 outbreak on medical staff.Methods: A cross-sectional study was conducted to examine the psychological impact of medical staff working in COVID-19 designated hospitals from February to March 2020 in China. We assessed psychological health problems using the Symptom Check List 90 (SCL-90).Results: Among 656 medical staff, 244 were frontline medical staff and 412 general medical staff. The prevalence of psychological health problems was 19.7%. The SCL-90 scores in frontline medical staff were significantly higher than that in general medical staff (mean: 141.22 vs. 129.54, P < 0.05). Furthermore, gender [odds ratio (OR) = 1.53, 95% CI = (1.02, 2.30), P = 0.042 for female vs. male] and the burden of current work [OR = 7.55, 95% CI = (3.75, 15.21), P < 0.001 for high burden; OR = 2.76, 95% CI = (1.80, 4.24), P < 0.001 for moderate burden vs. low burden] were associated with increased risk of poor psychological status.Conclusions: Medical staff experienced a high risk of psychological health problems during the outbreak of COVID-19, especially for frontline medical staff. Psychological health services are expected to arrange for medical staff in future unexpected infectious disease outbreaks.

General information

The UMHT is a specialised program developed to train frontline professionals on high-quality and evidence-based responses to the mental health needs of the population they serve. Police officers, emergency responders, social services workers, educators, pharmacists, priests, and other professionals daily interact with a substantial number of people. Whereas their professional roles imply working with people in crisis who experience strong emotions and require support, a high level of mental health awareness and skills to manage mental health issues are needed. Therefore, UMHT was developed as an educational instrument for Ukrainian frontline professionals to raise their mental health awareness, reduce stigma toward people with mental disorders and develop particular skills for giving support.

The training is called Universal because its 5-step model offers a standard frame for interaction with people with mental health issues. Also, it is Universal because it is suitable for different types of frontline workers – the general interaction structure is not changing, only the set of relevant mental health conditions.

The Mental Health Training for Frontline Professionals (UMHT) was developed in 2021 and piloted in 2021-2023 within the context of the Mental Health for Ukraine Project (MH4U), implemented in Ukraine by GFA Consulting Group GmbH (donor - Swiss Confederation). The University of Luxembourg, with the support of the European Commission through the MSCA4Ukraine fellowship scheme by the Alexander von Humboldt Foundation (AvH) for premier investigator Viktoriia Gorbunova, is leading a full-scale efficacy study of the UMHT in 2023-2025.

Data and file overview

Three efficacy measurements were used in the outcome assessment: readiness to interact with people with mental health issues at work, mental health awareness, and mental health proficiency.

Readiness to interact with people with mental health issues at work

To measure the changes in readiness to interact with people with mental health issues at work (according to the 5-step model), all participants self-assessed their general readiness as well as readiness to do particular actions according to the 5-step model on a five-point scale (from 5 - "absolutely ready" to 1 - "absolutely not ready").

In the instruction, participants were asked: "Reading the next statements, please assess your readiness for a different kind of interaction with people with mental health conditions. The scale is from 1 to 5, where 1 is the absolute absence of readiness, and 5 – is the absolute readiness".

The next set of statements was proposed to participants:

Readiness to interact with people with mental health issues at work (general readiness).

Readiness to recognise mental health conditions (readiness for step 1 of the 5-step model).

Readiness to initiate and lead conversation with a person with mental health issues and his/her caregivers (readiness for step 2).

Readiness to support a person with mental health issues and his/her caregivers (readiness for step 3).

Readiness to refer a person with mental health issues, and his/her caregivers, to professional support (readiness for step 4).

Readiness to ensure that professional help is received by a person with mental health issues and his/her caregivers (readiness for step 5).

Mental health awareness

Mental health awareness assessment was based on the KAP (knowledge, attitudes, and practices) model (Andrade et al., 2020). There is the experience of using such KAP-based surveys in Ukraine (Quirke et al., 2021). Based on the KAP model, a short survey was developed related to the knowledge about mental health issues, attitudes toward people with mental health disorders, and practice of interaction with them.

Knowledge regarding people with mental disorders was assessed with the query: "Choose the statements that apply to people with mental health disorders" (max = 8 scores, where each score was awarded either for a choice of a correct statement or for a non-selection of a wrong statement):

They are dangerous to the people around them.

They are themselves guilty of their condition.

They are incapable of true friendships.

They can work.

By appearance, it is clear that the person is not all right.

Anyone can have a mental disorder.

Mental disorders are incurable.

Most people with mental disorders can recover.

Attitude towards people with mental issues was assessed with the question: "What is the best way of behaviour for people with mental health issues?" (max = 8 scores):

Do not tell anyone about their condition.

Discuss everything with a doctor, but do not inform relatives.

Hide this information at work/school.

Tell loved ones and ask for help from specialists.

Hide it from the family.

Live among those like themselves.

Should not marry and have children.

The question for assessment practices of interactions with people with mental disorders: "What is the proper way of interactions with people with mental health disorders?" (max = 9 scores):

You would better avoid any contact with them.

You shouldn't allow them to make any decisions.

You would better avoid working with them in one team or performing tasks together.

You should be careful about conversations with them.

You should be ashamed and try to hide the fact you have a relative with a mental health disorder.

They should have the same rights as anyone else.

It is normal to have a friend with a mental health disorder.

It is normal to marry a person with a mental health disorder.

You should treat them with care and sympathy.

Practices of care about people with mental health issues were analysed with the question: "What is the best way to care about people with mental health issues?" (max = 6 scores):

In a psychiatric hospital where they are under supervision and control (psychiatrist).

Outside the hospital in specialised centres or privately (psychologist, psychotherapist).

Alternative methods of treatment (traditional medicine, homoeopathy, vitamins, massage).

Normal family relationships is the best treatment.

Do not waste energy, it is not possible to cure mental disorders.

At the primary level of health care (family doctor, paediatrician, general practitioner).

Mental health awareness scores were collected as the sum of scores for each scale.

Mental health proficiency

Mental health proficiency, as the ability to recognise mental health disorders' symptoms, was assessed by the tests that include correct and non-correct symptoms. Three true and two false symptoms (based on DSM-5) were offered for selection in each case. Mental health proficiency was estimated as the sum of the correct choices of symptoms for every disorder learned by participants. For instance, the participants who worked during the training with depressive disorders should choose all appropriate parameters among depressed mood, markedly diminished interest or pleasure in almost all activities, excessive or inappropriate feelings of worthlessness or guilt, inattention as, difficulties following instructions and failure to finish tasks, restlessness as fidgeting with or tapping hands or feet or squirming in the seat.

Additional one-month follow-up questions

Additional questions for the one-month follow-up test were: "Did you work after the training with people with mental health issues that you studied?", "What kind of the issues?", "Did you use training knowledge and skills?", "Which knowledge and skills did you use in particular?"

Sharing and accessing information

Information (raw anonymized data) is openly available through Zenodo. It is possible to use the information with research aims to evaluate UMHT or compare data with other similar programs. Our research team kindly asks to notify the contact person (Viktoriia Gorbunova) about any usage of the dataset.

Methodological information

The study was quasi-experimental (no complete randomization was possible at this piloting stage). Two groups were involved: the experimental group (received UMHT) and the control group (no training, waiting list).

The pilot trial of UMHTs' efficacy was conducted with 307 frontline professionals divided into 24 training groups (social workers (12 groups, 128 persons), educators (4, 63), police officers (4, 60), priests and clerics (1, 15), military volunteers (1, 12), workers of occupation centres (1, 13), emergency workers (1, 16)). All participants were recruited for training by their team leaders, who were informed about training possibilities by letters sent from the training developers. The only requirement for participation was working in the field with people.

The control group included 211 persons with the same occupation background who participated in training later (waiting list). The control group consisted of social workers (97 persons), educators (32), police officers (40), priests and clerics (12), military volunteers (13), workers of occupation centres (7), and emergency workers (10).

Data-specific information

Excel file (UMHT_dataset_pilot_trial.xlsx) containing four pages.

1. Page "Training groups_before UMHT". Contains the answers to questionaries completed by UMHT training participants before the training.

2. Page "Training groups_after UMHT". Contains the answers to questionaries completed by UMHT training participants immediately after the training.

3. Page "Training groups_after one month". Contains the answers to questionaries completed by UMHT training participants the month before the training.

4. Page "Control group_before-after". Contains the answers to questionaries completed by UMHT control group participants (waiting list) before and after the training.

General information

The Universal Mental Health Training for Frontline Professionals (UMHT) is an educational program developed and piloted in Ukraine in 2021-2023 to bridge the mental health gap between Ukrainians’ support needs and the health system's answers. Frontline professionals were trained to interact, support and refer for the professional help people with mental health conditions.

The UMHT was developed in 2021 and piloted in 2021-2023 within the context of the Mental Health for Ukraine Project (MH4U), implemented in Ukraine by GFA Consulting Group GmbH (donor - Swiss Confederation). The University of Luxembourg, with the support of the European Commission through the MSCA4Ukraine fellowship scheme by the Alexander von Humboldt Foundation (AvH) for premier investigator Viktoriia Gorbunova, is leading a full-scale efficacy study of the UMHT in 2023-2025.

Pilot trial data available: https://zenodo.org/records/10410525

Data and file overview

To assess the demand, acceptability, adaptability and extendability of the UMHT as the focus areas of the programme’s feasibility were used statistics on the actual use of the program, as well as data from satisfaction and usability surveying of 144 program deliverers and 714 trained frontline professionals.

To assess programme demand, we measured the actual use of the program with statistics on the number of trainings conducted with donor support in terms of organisation and financing and independently on request of different organisations.

As program acceptability measurement was chosen, training content and delivery satisfaction by its trainers as deliverers and FLP as its recipients through satisfaction surveying. All audiences were asked to assess on a 5-grade scale whether training materials were structured and clear, whether the balance between theory and practice was kept, whether there were enough examples and explanations, whether answers to participants’ questions were full and clear, whether trainers where careful of sensitive topics and mindful of stigma and whether presentations and following material were high quality and perceptibility.

The same scale was applied for self-assessing the ToTs participants’ post-training boost of preparedness to lead UMHT in terms of knowledge, skills and general readiness: “My understanding of the topic of interactions with people with MH issues has increased”; “My skills to lead UMH trainings and supervisions was mastered; “My readiness to lead UMH trainings and supervisions has increased”. FLP assessed their with the questions: “My understanding of the topic of interactions with people with MH issues has increased”; “My skills to interact with people with MH issues was mastered; “My readiness to work with people with MH issues has increased.

Also, future trainers and FLP were asked to name knowledge and skills they would like to strengthen and give suggestions on changes in the training materials and process to improve it.

Additionally, data were taken from the accreditation assessment of UMHT trainers' knowledge and skills observed in the training delivery process. In particular were assessed subject knowledge, organisational skills, instrumental skills, motivational skills, and ethical skills.

The adaptability of the program was measured through its comparative usability by different groups of FLP, specifically with questions: “Did you work with people with mental conditions after the UMHT?”; “Did you use the knowledge and skills gained during the UMHT?”; “If yes, what knowledge and skills gained during the UMHT you were able to use?” The list of knowledge and skills consists of those needed on each step of the UMHT model and purposely targeted during the training: recognise MH condition, validate MH condition with a person / their caregivers, support a person, refer for professional help, and ensure the reference was successful.

Additional information, such as the content of supervision requests from the UMHT trainers, was driven from supervision reports. In general, there were six possible types of supervision: organisational supervision (issues related to the organisation of the training process and supervision of training participants), content-related supervision (issues related to the need to expand/deepen knowledge about a specific disorder or other training topic), instrumental supervision (issues related to the need to practice specific skills to lead training), navigational supervision (issues related to the need for additional motivation of participants, management of difficult situations, conflict-solving), technical supervision (issues related to the use of digital applications during training and other technical aspects), motivational supervision (questions related to psychological readiness to lead training, burnout and general need for support).

The comparative usability for modules centred on MH disorders and newly developed modules centred on mental health crises (aggressive behaviour, self-harm behaviour, suicide & life-threatening behaviour, unusual & disorganised behaviour) was used for extendability measurement of the UMHT.

Sharing and accessing information

Information (raw anonymised data) is openly available through Zenodo. It is possible to use the information with research aims to evaluate UMHT or compare data with other similar programs. Our research team kindly asks to notify the contact person (Viktoriia Gorbunova) about any dataset usage.

Methodological information

The aim of the present study was to assess the feasibility of the UMHT as a public mental health promotion&prevention intervention on the base of its demand as well as users’ satisfaction and programme potential to be adjustable for different groups of frontline professionals and to be open to modifications due to design and content features. Therefore, the study focuses on the relevance of the UMHT to social demand, its acceptability, adaptability and capacity for further development and modification.

The UMHT was disseminated by the Training of Trainers (ToT) approach, highlighted in the mhGAP Operation Guide (WHO, 2018). To assess the feasibility of the UMHT, we surveyed the satisfaction and usability of the programme, its trainers (mental health professionals trained to deliver the UMHT to different groups of frontline professionals), and frontline professionals (FLP) as programme recipients. Generally, the analysis used answers from 144 UMHT mental health professionals who intended to become UMHT trainers and 714 frontline professionals (social workers (203 persons), educators (152), police officers (122), workers of occupation centres (58), emergency responders (52), military volunteers (34), pharmacists (37), librarians and museum workers (29), priests and clerics (27)).

Data-specific information

Underlying data are available in the Excel file (UMHT_data_full set_ZENODO_v.2.xlsx), containing five pages.

1. Page " Satisfaction". Contains the answers to questionaries completed by UMHT training participants after the training.

2. Page "Feedback and suggestions UA". Contains the qualitative data, provided by UMHT training participants immediately after the training in Ukrainian language (original).

3. Page "Feedback and suggestions EN". Contains the qualitative data, provided by UMHT training participants immediately after the training in English language (translation).

4. Page " Usability". Contains the answers to questionaries completed by UMHT training participants the month before the training.

5. Page " Supervision reports". Contains the data of assessment of the UMHT participants by UMHT trainers/supervisors.

Extended data are available in the PDF file (UMHT Feasibility Extended Data.pdf), containing 38 Tables with detailed statistics, calculated based on the Underlying Data.

High levels of chronic and recurrent workplace stressors can profoundly impact the physical, mental, and emotional health and well-being of the health care workers. Research and interventions specially related to various mindfulness-based interventions have been shown to be beneficial at countering the negative effects of workplace stressors in the healthcare environment. While these interventions have primarily focused on front line healthcare workers, nurse managers have received less attention. In this randomized controlled trial, a sample of nurse managers and assistant nurse managers employed across an academic medical center were randomized into intervention and wait-list control groups. According to their assigned group, they engaged in a commercially available virtual reality mindfulness intervention (TRIPP) during their work day, three times a week for 15 minutes. Over the course of an eight week period, participants in each group engaged with the virtual reality mindfulness in..., Consent and baseline questionnaires, PSS-10, MBI-HSS, CD-10, and UWES-9, for all participants (both Intervention and Wait-List control groups) were completed prior to initiation of the intervention for the Intervention Group, via the participant’s smartphone or computer, accessed via a REDCap link. Participants in the Intervention Group (treatment group) received a VR headset, instructions on use of the VR headset, hand controllers, and the virtual reality mindfulness program; contact information for questions, concerns, or discomfort with the virtual reality technology and software was provided to each participant by a member of the research team. Intervention Group participants were instructed to use the virtual reality mindfulness intervention three times a week during work hours; weekly participation occurred within their worksite office space. At the end of the first eight-week intervention period, all participants (both Intervention and Wait-List Control groups) completed the PSS-..., # Data related to a randomized controlled trial to evaluate the impact of a virtual reality mindfulness intervention for nurse managers in an academic medical center

Dataset DOI: 10.5061/dryad.w6m905r2h

Description of the data and file structure

Title of Dataset: A Randomized Controlled Trial to Evaluate the Impact of a Virtual Reality Mindfulness Intervention for Nurse Managers in an Academic Medical Center

Description of the data and file structure

The csv files contains the study data corresponding to Demographics and Quantitative Outcomes Data. The .R file contains the R Statistical Software code used for analysis of outcomes for PSS-10, MBI-HSS, CD-10, UWES-9

Spreadsheet (1) - Demographic Data (limited to 3 identifiers):Â Spreadsheet_1_S5784AVirtualReality_DATA_2025-10-05_1739_de_identified_DRYAD.csv

• Record ID - Subject Study ID

Demographics:

• Age range- range of participant (in years). Includes ranges 21-30 years, 31-4..., All study participants signed an institutional IRB-approved study consent before participation. The consent included the statement: "If you consent to be in this study, your de-identified information (your survey responses) may be used or shared for future research without your additional consent and may be posted to a public repository for study replication or manuscript publication. Information that could directly identify you will never be included."This submission reflects the raw dataset that includes de-identified demographic indicators (age, race/ethnicity, number of direct reports from REDCap-exported to Excel) for intervention and wait-list control groups of nurse managers and assistant nurse managers who participated in an eight-week worksite virtual reality mindfulness intervention. The responses (number) for each outcome measure question align with the Likert scale range specific to each measure; this de-identified file can be used freely.

This study aimed to analyze stressors to which medical staff is vulnerable due to the coronavirus disease 2019 (COVID-19) pandemic. It also imposes a hierarchy on complex relations among stressors for excavating underlying structure and builds a model of interrelationships contrasting reality. The design of this study comprises a literature survey, data collection from primary sources, and analysis. Stressors have been explored from within current published/unpublished literature and validated by experts through approval vote. Data were collected from the focus group (panel of experts), and interpretive structural modeling (ISM) was used as the research methodology. Findings of ISM are avowed through “cross-impact matrix multiplication applied to classification” (MICMAC) analysis. As a result of the literature survey, a list of stressors was generated, and a total of 19 stressors qualified as representative of the phenomenon. The results of ISM show that two stressors (i.e., “unavailability of proper personal protective equipment (PPE)” and “lack of proper communication”) emerged as the most critical stressors since they occupy the bottom of the model, whereas, four stressors (i.e., “anxious about isolation/quarantine,” “subject to violent crimes,” “feeling frustrated and powerless,” and “exhausting shifts/hours without clear end”) are relatively less critical since they occupy the top of the model. The rest of the stressors occupy the middle of the model and therefore, have moderate-severe effects on frontline soldiers. The results of MICMAC show that the stressor “subject to violent crimes” is classified in the dependent cluster and the remaining fall in the linkage cluster but no stressor falls in independent and autonomous. Overall results indicate that all stressors are relevant to the phenomenon under this study, but they are currently not settled. This study is invaluable for policymakers, frontline soldiers, researchers, the international community, and society since it provides a lot of new information that is helpful in refining strategies and combating influential stressors.

In long-term care, frontline supervisors play a central role in direct care workers' (DCW) job quality and turnover and are critical to the implementation of management changes. To better understand supervisors' perceptions of management practices, the quality of supervision, and the effect on DCW turnover and job quality, the Office of the Assistant Secretary for Planning and Evaluation in the United States Department of Health and Human Services contracted with Pennsylvania State University to conduct this survey of supervisors participating in the Better Jobs, Better Care (BJBC) demonstration. Funded by the Robert Wood Johnson Foundation and The Atlantic Philanthropies, the BJBC demonstration -- which took place in Iowa, North Carolina, Oregon, Pennsylvania, and Vermont -- tested innovative policy and practice models designed to improve the quality of DCW jobs in an effort to improve recruitment and retention of these workers and strengthen capacity to meet future demand for long-term care. Frontline supervisors were interviewed from the four types of facilities and agencies that participated in the demonstration: skilled nursing facilities, assisted living facilities, home care agencies, and adult day service providers. The survey explored the supervisors' job responsibilities, formal training, job satisfaction, and thoughts about quitting. It investigated the culture of the organizations in which the supervisors worked, probed for problems with the supervisors' jobs, assessed how rewarding the supervisors felt their jobs were, inquired as to whether the supervisors felt respected by their clients, DCWs, and managers, gauged the supervisors' assessments of the overall competency level of the DCWs in their organizations, and explored the supervisors' beliefs about managerial support for the BJBC project, how well the BJBC programs were executed, and whether the overall impact of the project was positive. In addition, the respondents were queried about management practices (e.g., rotation of assignments to different services or units, mechanisms to handle employee concerns, and approaches used to handle poor performance or negative behaviors among employees). They were also asked about DCW training, mentoring, and career ladder programs, DCW participation in patient/resident/client care plans, and communication among DCWs and between DCWs and their supervisors. Respondents were also asked what was the most important thing that their employer could do both to improve the jobs of DCWs and to improve their own ability to do their jobs as supervisors of DCWs. Additional information collected by the survey includes the supervisors' age, sex, race, Hispanic origin, educational attainment, nursing degree or license (LPN, RN, Diploma RN, BSN, MSN, or Advanced Practice Nurse), wages, and health insurance coverage. This collection comprises three data files: (1) Supervisor Identification Instrument Data, (2) Supervisor Survey Data, and (3) Clinical Managers Who Are Also Supervisors Data. The first file contains information collected by the Supervisor Identification Instrument that was submitted to the clinical manager at each BJBC provider organization. This instrument instructed clinical managers to name all of the supervisors in their organization and to indicate which supervisory responsibilities each one performed. The second data file contains the responses to the Supervisor Survey questionnaire.The third data file contains the responses of clinical managers who also functioned as supervisors in their organization. These clinical managers responded to the same questions in the Supervisor Survey questionnaire, except for ten questions that were worded somewhat differently.

This dataset contains Taiwan citizen Covid-19 vaccinations in the last couple of months.

Column description:

• Estimated amount of remaining dose/bottle: = (actual delivery amount - actual amount of vaccinations)
• Location: name of the country (or region within a country)
• Vaccination_group: Taiwan government provide publicly funded vaccine for 10 prioritized groups, including healthcare workers; central and local government epidemic prevention personnel; frontline workers with a high risk coming into contact with COVID-19; those who need to travel abroad; law enforcement officers and firefighters; volunteers, long-term caretakers, and care recipients at social welfare organizations; national security personnel; adults ages 65 and above; adults ages 19 to 65 with life-threatening conditions, rare diseases, or a history of serious illness; and Adults between the ages of 50 and 64
• Acceptable_vaccine_manufacturer: the vaccine manufacturer that people are willing to take

Reference

https://nidss.cdc.gov.tw/en/nndss/disease?id=19CoV "Taiwan%20Covid-19%20Dashboard%20-%20Vaccination">https://covid-19.nchc.org.tw/dt_002-csse_covid_19_daily_reports_vaccine_city2.php?language=zh-tw&language=en

Emergent themes and subthemes for included studies.

1. Create a model to predict the credit card charged amount using the variables in the dataset. You are free to use any variables available in the dataset (we will suggest using numerical data). Were you able to build an accurate and reliable model? Which variables are (or are not) relevant in predicting the amount of credit card charged?
2. Create a clustering model with three (3) clusters using the following variables. Explain the characteristics of each cluster. Where do most Loans ‘R Us customers come from/located? What recommendation(s) can you make from the clusters to increase the number of Loans ‘R Us customers?
3. To help the frontline workers assess credit card fraud, you will need to create a classification model (K nearest neighbor, Naïve Bayes, etc.) based on the dataset available to you. You are free to use any variables in the dataset. One of your employees suggests splitting the dataset into two (training and testing) to create this classification model. Due to the unevenness of the data, a stratified approach to dividing the dataset may be needed. a. Explain what variables you used from the dataset, the classification method utilized, and how the dataset was divided into training and test data. b. How good is your classification model? i. How many were predicted as fraud was actually fraud ii. How many were predicted as fraud was actually not a fraud iii. How many were predicted as not a fraud was actually not a fraud iv. How many were predicted as not a fraud was actually fraud c. Explain how you would use this newly created model to help frontline workers make decisions based on the prediction made by the model.

Column Info trans_date_trans_time: Transaction DateTime merchant: Merchant Name category: Category of Merchant amt: Amount of Transaction city: City of Credit Card Holder state: State of Credit Card Holder lat: Latitude Location of Purchase long: Longitude Location of Purchase city_pop: Credit Card Holder's City Population job: Job of Credit Card Holder dob: Date of Birth of Credit Card Holder transmun: Transaction Number merch_lat: Latitude Location of Merchant merch_long: Longitude Location of Merchant is_fraud: Whether Transaction is Fraud (1) or Not (0)

Heads‑Up Display Pick Guidance Market Outlook

According to our latest research, the global Heads-Up Display (HUD) Pick Guidance market size reached USD 2.68 billion in 2024, reflecting robust demand across industries. The market is anticipated to grow at a CAGR of 13.2% from 2025 to 2033, culminating in a projected market value of USD 7.56 billion by 2033. This growth is primarily fueled by the increasing adoption of advanced visualization technologies in logistics, manufacturing, and automotive sectors, as organizations seek to enhance operational efficiency and worker productivity through real-time information delivery.

One of the major growth drivers for the Heads-Up Display Pick Guidance market is the rapid digital transformation occurring within warehouse and logistics operations worldwide. The surge in global e-commerce and the resulting pressure on supply chains have necessitated the adoption of innovative solutions that streamline order picking and inventory management. HUD pick guidance systems, leveraging technologies such as augmented reality and optical waveguides, enable workers to access critical picking information directly in their line of sight. This not only reduces errors and accelerates picking speed but also minimizes training time for new employees. As companies strive to meet rising customer expectations for faster and more accurate deliveries, the demand for HUD pick guidance solutions is expected to intensify, particularly among large distribution centers and fulfillment hubs.

Another significant factor propelling market growth is the technological evolution of HUD systems themselves. The integration of augmented reality, lightweight wearable devices, and advanced projection technologies has made these systems more user-friendly, durable, and cost-effective. Modern HUD pick guidance solutions now offer seamless integration with warehouse management systems (WMS) and enterprise resource planning (ERP) platforms, facilitating real-time data synchronization and analytics-driven decision-making. Furthermore, advancements in battery life, wireless connectivity, and ergonomic design have enhanced the adoption rate among frontline workers, reducing fatigue and improving overall safety. As R&D investments continue to focus on expanding the capabilities of HUD systems, the market is poised for further innovation and expansion.

The Heads-Up Display Pick Guidance market is also benefiting from the increasing emphasis on workplace safety and employee well-being. By providing hands-free, heads-up access to relevant information, these systems help reduce distractions and physical strain associated with traditional handheld devices or paper-based picking lists. This is particularly important in high-volume environments such as automotive assembly lines, aviation maintenance, and large-scale retail warehouses, where efficiency and safety are paramount. Regulatory bodies and industry standards are also encouraging the adoption of technologies that promote ergonomic working conditions, further driving market growth. As organizations seek to create safer and more productive workplaces, the adoption of HUD pick guidance solutions is expected to become a standard practice across multiple verticals.

From a regional perspective, North America currently dominates the Heads-Up Display Pick Guidance market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The strong presence of leading technology providers, early adoption of digital solutions in logistics and manufacturing, and robust investments in R&D have positioned North America as a key growth engine. Europe is witnessing steady growth, driven by the expansion of e-commerce and the increasing focus on automation in supply chain operations. Meanwhile, Asia Pacific is emerging as a high-growth region, fueled by rapid industrialization, expanding retail infrastructure, and growing investments in smart manufacturing technologies. As these trends continue, regional dynamics are expected to play a pivotal role in shaping the competitive landscape and future growth of the market.

Product Type Analysis

The Heads-Up Display Pick Guidance market is segmented by product type into Wearable HUDs, Fixed-Mount HUDs, and Portable HUDs, each offering distinct advantages and catering to specific operational environments. Wearable HUDs, such as smart glasses and head-mounted displays, have gained significant traction i

Characteristics of contacts in Bukoba District, Kagera region from March to May 2023 (n = 212).

BackgroundSkin-related neglected tropical diseases (sNTDs) remain a significant public health challenge in Cameroon, where limited resources, training, and infrastructure hinder early diagnosis and management. The World Health Organization (WHO) has developed the SkinNTDs app version 3.0 as a digital solution to assist frontline healthcare workers (FHWs) in recognizing and managing sNTDs. As utilization will rely on a high degree of awareness among FHWs, a dedicated and effective marketing plan is required. This study describes the design, implementation, and evaluation of a structured marketing plan to promote the app among FHWs in Cameroon.MethodsWe conducted a pilot quasi‑experimental before‑and‑after study comparing three 6‑month phases—pre‑campaign, campaign, and post‑campaign. The multi‑channel marketing campaign combined communications via WhatsApp, in‑person training sessions, video presentations, and email outreach. Google Play Console Analytics provided monthly metrics on store listing visits, downloads, installations, uninstalls, and retention.ResultsDuring the campaign (1 April 2024–30 September 2024), store page visits totaled 961, yielding 616 downloads (conversion rate = 64.1%) and the app was installed 751 times; new-user acquisition exceeded 81.6%. Net installs surged by 227.3% in April and 140.4% in May, with retention above 88%. ANOVA revealed significant period effects on growth rate (p = 0.007, ε² = 0.591), loss rate (p = 0.011, η² = 0.452), churn rate (p = 0.003, η² = 0.532), and retention rate (p = 0.003, η² = 0.532), with campaign performance superior to pre‑ and post‑campaign phases. Interrupted time series analyses found gradual adoption and sustained retention following intervention start, but significant decrease at campaign end.ConclusionA context‑adapted, multi‑channel marketing strategy markedly improved adoption and retention of the WHO SkinNTDs app among Cameroonian FHWs. Digital (WhatsApp, videos) and face‑to‑face (training) channels were complementary. Sustained integration into routine health activities and automated re‑engagement tools are recommended to maintain long‑term use and inform scale‑up in other endemic settings.

Through its multidisciplinary team, provides tailored training and support services for young persons with high-functioning autism to meet their individualised needs in coping with the challenges during their transition into adulthood. Support Centre for Persons with Autism also offers support services for their parents/carers; and provides professional consultation service and training for frontline workers serving persons with autism.

Localizing Commitments, Challenges, and Insights on the Road to Immunization Agenda 2030: Responses from 6,185 national and sub-national staff (Immunization Agenda 2030 Full Learning Cycle, 7 March - 20 June 2022)

Abstract

This data set contains survey responses from over 6,000 national and subnational immunization staff who applied to participate in a 2022 Full Learning Cycle (FLC) learning programme of The Geneva Learning Foundation (TGLF), intended to contribute to the Movement for Immunization Agenda 2030. The 95-item questionnaire collected information on respondents' commitment to the movement's principles, demographics, work challenges, motivation and learning culture, and the impact of COVID-19 on routine immunization. The purpose was to understand applicants' priority challenges and readiness to engage in peer learning to advance country and global immunization goals. Questions addressed consent, identity confirmation, COVID-19 vaccination status, employer, role, system level, past participation in the sponsoring organization's programs, work and wellbeing, difficulties with COVID-19 vaccination, outbreak response, gender equity, and reaching zero-dose children. Applicants identified one priority challenge in their work that they would seek to address through the program. This data set offers insights into frontline perspectives on strengthening immunization programs. Secondary analysess were performed in 2022 and 2023 to illuminate human resource issues, gender barriers, pandemic recovery, and peer learning for change.

Research Audience

Education and global health researchers with interest in human resources for health (HRH) and the characteristics, priority challenges, and experiences of national and sub-national immunization staff participating in the Movement for Immunization Agenda (IA) 2030.

Credits

Author

The Geneva Learning Foundation (TGLF) 18 Avenue Louis Casaï CH1209 Geneva, Switzerland research@learning.foundation

Principal Investigator and Corresponding Author

Reda Sadki, TGLF reda@learning.foundation

Project Partners

• Biostat Global Consulting (BGC)
• Bridges to Development
• Centre for Change & Complexity in Learning (C3L)

Partners' Roles and Responsibilities

• Design: TGLF
• Implementation (sample collection): TGLF
• Processing: TGLF, C3L
• Anonymization: BGC
• Data cleaning: Bridges to Development
• Submission: TGLF
• Maintenance of learning analytics database where data are stored: C3L

Funding

Wellcome Trust, Bill & Melinda Gates Foundation (BMGF)

Recommended Citation

The Geneva Learning Foundation, 2023. Full Learning Cycle (2022) Application for national and sub-national immunization staff to identify challenges and join the Movement for Immunization Agenda (IA 2030) (Version 1.0) [Data Set]. The Geneval Learning Foundation. DOI:https://doi.org/10.5281/zenodo.8199552

File List

IA2030_EN_FLC_2022_Application_Survey.README.md (this document)

20220211.IA2030-EN Movement application-FINAL.docx: List of questions included in the questionnaire. (Note: skip patterns are not shown.)

IA2030_EN_Application_Survey_Dataset.xlsx: English version of anonymized Application Survey Dataset. Version 1: Geneva Learning Foundation, 11 August 2023. (6,669 observations; 58 variables)

Related Data Sets

This is a subset of data collected by The Geneva Learning Foundation (TGLF) during the first IA2030 Full Learning Cycle (FLC). The complete IA2030 Application Survey data set is more comprehensive, and includes information such as respondents' gender, employer, professional role, country, and health system level, as well as responses to open-text questions.

Researchers who would like to analyze the full set of unredacted responses are invited to contact the Geneva Learning Foundation to inquire about a Data Sharing Agreement that would stipulate conditions of access (insights@learning.foundation).

The Geneva Learning Foundation, 2023. Value Creation Stories (VCS) weekly feedback survey, 2022 Full Learning Cycle (FLC) of the Movement for Immunization Agenda 2030 (IA2030) (Version 1.0). [Data Set]. The Geneva Learning Foundation. DOI: https://doi.org/10.5281/zenodo.7763922

Additional data sets for the first Full Learning Cycle (FLC) of the Movement for Immunization Agenda 2030 (IA2030) are available from TGLF's Insights Unit ().

Survey Purpose

The Immunization Agenda 2030, the global immunization strategy for 2021-2030, set ambitious targets for global immunization coverage and other key indicators (World Health Organization [WHO], 2023a).

In response to the WHO Director-General's call for a social movement to ensure immunization remains a priority for global and regional health agendas and promote broad societal support for immunization (WHO, 2023b), TGLF, working with its global community of over 35,000 alumni, developed a learning programme intended to contribute to a “Movement for Immunization Agenda 2030 (IA2030)”.

In addition to participating in structured peer learning activities, applicants made a pledge to work towards IA2030 and their country's goals, adhere to the IA2030 core principles, and to provide support to their peers making similar commitments.

The purpose of the IA2030 Application Survey was to collect demographic and organizational information from immunization workers who work at the national or sub-national level interested in applying for the 2022 Learning Cycle and for membership in the Movement for IA2030.

Survey Questionnaire

The survey questionnaire consisted of both quantitative (Likert) and qualitative (open-text) responses to 95 questions documenting respondents' commitment to joining the Movement for IA2030 and adhering to relevant principles, demographic characteristics, information about work history and role, work and well-being, learning culture and performance, COVID-19 recovery efforts through vaccination campaigns and routine immunization (including outreach), priority work-related challenges, and most important reason for wanting to join the Movement for IA2030.

Survey content was informed by TGLF's six years of experience working with thousands of immunization workers from over 90 countries.

The survey was administered in English and French. While most of questions were required, several items, including questions about work and well-being and COVID-19 vaccination status, were optional.

Question Scaling and Response Options

Most questions were asked with a 'select one response' instruction, but several encouraged the respondent to 'select all that apply'. - AP_CAR_20 Which of these job categories apply to you? - AP_ENV_55 Where you work, what strategies have been put in place to reach under immunized or zero-dose children? - AP_CHA_63 Is your challenge related to any of these? - AP_ENV_78 What actions are being taken at your level of the health system to strengthen RI or PHC that specifically takes advantage of some aspect of COVID-19 vaccine introduction? - AP_ENV_87 Select all activities used for catch-up. - AP_ENV_91 What were the disruptions related to?

Each person's several responses are stored in a single text variable and separated by commas. Some data management will be necessary to divide these strings of text into individual variables to represent each response option.

Overview of questionnaire for respondents

The following information was shared with all applicants to provide an overview of the questions and their rationale.

First, we ask you for: - Consent to share your data, to confirm your supervisor’s support, and to make commitments to follow country and WHO guidelines on COVID-19 and immunization - Your legal name and birthdate to confirm your identity for certification. - Your WhatsApp number to connect you with other participants in the Movement. - Your organization, role, and health system level, and if you are a TGLF alum. - We ask you about your work and well-being: Before we ask you about the challenges you face, we ask about your work and well-being, especially your motivation and how learning is being supported where you work. - We ask about the challenges you face: In 2020, global immunization coverage levels for infants dropped back to 2009 levels. It is like we lost 11 years of hard work. So we ask you about the challenges you face: COVID-19 vaccination, epidemic outbreaks (measles, yellow fever, etc.), gender barriers, and zero-dose children. - We ask you to pick the challenge that you will work on in the Movement: Then we ask you to identify your most difficult and important challenge. This is the one that you will focus on in the Movement. (You can always change later.) - Are you truly committed to learning with colleagues from all over the world? Because the Movement is about learning, sharing experience, and collaborating with others, we ask you to confirm to what extent this is what you want to do. - We ask you to share your successes, ideas, and lessons learned: Because we know that you have many strengths, we ask you if you want to share a success story, and idea, or a lesson learned with colleagues. - We ask you if you want to help build and shape the Movement for IA2030: We ask you if you want to join the Organizing Committee to help build the Movement for Immunization Agenda 2030. - Global partners request your help: Finally, we ask you to answer questions that IA2030 global partners are specifically interested in, about the effect of COVID-19 on routine immunization, catch-up activities, and your own COVID-19 vaccination. (You can choose to skip these questions.)

Specific questions that respondents were encouraged to reflect upon before writing out their answers

• Tell us more about your work and well-being. What are the worst and best parts of your job? Where do you find the motivation to continue your work? What helps you feel involved in your