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.

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

Digital Pick Lists for Kitting Market Outlook

According to our latest research, the global Digital Pick Lists for Kitting market size reached USD 1.42 billion in 2024, with a robust compound annual growth rate (CAGR) of 13.7% projected from 2025 to 2033. This rapid expansion is anticipated to elevate the market to USD 4.10 billion by 2033. The primary driver behind this growth is the rising adoption of digital transformation initiatives across manufacturing, warehousing, and e-commerce sectors, which are increasingly prioritizing efficiency, accuracy, and agility in their kitting operations.

The growth trajectory of the Digital Pick Lists for Kitting market is largely fueled by the escalating demand for automation and real-time inventory management solutions. As global supply chains become more complex, organizations are seeking advanced technologies to streamline their kitting processes, reduce manual errors, and optimize resource allocation. Digital pick lists, integrated with advanced software and hardware, enable seamless communication between warehouse management systems and frontline workers, ensuring that the right components are picked efficiently and accurately for assembly or shipment. This not only minimizes costly errors but also enhances operational throughput and customer satisfaction, which are critical competitive differentiators in today’s fast-paced markets.

Another significant factor contributing to the market’s expansion is the proliferation of e-commerce and omni-channel retailing, which demand rapid and error-free order fulfillment. Digital pick lists for kitting are pivotal in enabling warehouses and distribution centers to manage high order volumes, frequent SKU changes, and complex kitting requirements. The ability to dynamically update pick lists, track inventory in real-time, and integrate with automated picking technologies such as robotics and wearable devices is driving adoption among retailers and third-party logistics providers. Furthermore, the ongoing labor shortages in logistics and manufacturing sectors are prompting organizations to invest in digital solutions that enhance workforce productivity and reduce dependence on manual labor.

The integration of emerging technologies such as artificial intelligence, machine learning, and the Internet of Things (IoT) is further accelerating the adoption of digital pick lists for kitting. AI-powered analytics can optimize pick paths, predict inventory needs, and automatically adjust kitting workflows based on real-time demand signals. IoT-enabled devices provide granular visibility into inventory movement and worker activity, enabling proactive decision-making and continuous process improvement. As organizations strive for greater agility and resilience in the face of market disruptions, the adoption of these intelligent digital solutions is expected to become increasingly prevalent, further propelling market growth over the forecast period.

From a regional perspective, North America currently leads the global Digital Pick Lists for Kitting market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The dominance of these regions is attributed to the high concentration of manufacturing, warehousing, and e-commerce activities, as well as the early adoption of digital technologies. However, Asia Pacific is expected to witness the fastest growth during the forecast period, driven by rapid industrialization, expanding e-commerce penetration, and increasing investments in smart manufacturing infrastructure across China, Japan, and India. Latin America and the Middle East & Africa are also showing promising growth potential as more organizations in these regions embark on digital transformation journeys to enhance operational efficiency and competitiveness.

Component Analysis

The Digital Pick Lists for Kitting market is segmented by component into software, hardware, and services. The software segment holds the largest marke

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.

Emergent themes and subthemes for included studies.

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

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.

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)

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.

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.

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

Abstract

What do we know about incentives and norms in health bureaucracies and service delivery points at various levels of a state in India? For example, the logic of economic theory suggests that governments should be direct providers of services when there is a role for attracting intrinsically motivated agents (Francois, 2000), but we have no empirical evidence on integrity and public service motivation among state personnel across different cadres of service delivery. The available research has focused on documenting evidence of weak incentives and low accountability for service delivery in the public sector, and thence on evaluating interventions targeted at strengthening incentives, such as making some part of pay conditional on performance indicators (for example, Singh and Masters, 2017). But what is available is barely scratching the surface of knowledge needed to help reform leaders think about how to structure government bureaucracies and assign tasks to leverage intrinsic motivation and to reduce reliance on high-powered incentives. Even when increasing the power of incentives has been shown to “work”, the authors of those findings concede that implementing optimal incentive contracts at scale can place significant demands on state capacity (Muralidharan and Sundararaman, 2011). There is even less evidence available about the incentives and motivation of mid-level bureaucrats within the health system, compared to a growing body of research on frontline providers such as doctors and community health workers. Finally, the logic of economic theory, and growing international evidence in support of it, further suggests that politics casts a long shadow on culture in the bureaucracy, but we have no rigorous evidence for this claim for India.

To address these knowledge gaps we designed and implemented a complex survey of multiple types of respondents across districts, blocks (administrative sub-units within districts) and village governments (Gram Panchayats or GPs) in Bihar, one of the poorest states of India and with some of the worst statistics of child malnourishment.

Geographic coverage

16 study districts, from among the 38 of Bihar, selected to represent the 9 administrative divisions of Bihar: Patna, Tirhut, Darbhanga, Kosi, Purnia, Saran, Bhagalpur, Munger, Magadh

Analysis unit

Households Health Staff Politicians Bureaucrats

Universe

Citizens, Within the category of citizens, the survey additionally targeted office-bearing members of women’s Self Help Groups (SHG) under a rural livelihoods program in Bihar known as Jeevika. Politicians Bureaucrats Public Providers of Health Services

Kind of data

Sample survey data [ssd]

Sampling procedure

Budget and implementation constraints required us to select a sample of districts rather than covering all 38 districts of Bihar. At the same time, we needed a large sample to be representative of the diversity within the state, and allow us to capture some variation across district-level institutional characteristics. These constraints led us to determine 16 as the number of districts in which to undertake the survey. The purposive selection of which 16 study districts, from among the 38 of Bihar, was made using the following criteria:

• represent the 9 administrative divisions of Bihar: Patna, Tirhut, Darbhanga, Kosi, Purnia, Saran, Bhagalpur, Munger, Magadh • represent both border and interior districts • select "old" and "new" districts (those which were created after 1991) because district age might matter in interesting ways for their capacity to deliver (to be discussed further) • select districts which might vary in historical institutions that shape norms.

We first explored an established literature in India which finds that there are persistent effects on current service delivery of the long-gone historical institution of the Zamindari system of land revenue (Pandey, 2010; Banerjee and Iyer, 2005). However, since all of the districts of Bihar are classified as belonging to the Zamindari system, we could not use this established measure of historical institutions in selecting the study districts. We then turned to a newer literature which examines the early construction of railway lines in the late 1800s in the United States and India as a potential source of institutional variation (Donaldson, 2018; Donaldson and Hornbeck, 2016; Atack, Haines and Margo, various). The 16 districts in our study include those through which passed the first railway lines in Bihar, and those that received railway lines a decade or so later.

Within each of the 16 districts, 4 blocks were selected using a random number generator,after stratifying by proximity to the main railway line. Within each block, 4 Gram Panchayats (GPs) were selected using a random number generator. However, in one block each in the districts of Lakhisarai and Buxar, 3 GPs instead of 4 were selected because the sampling protocol required a sufficient number of replacement respondents to be available, and these districts only had 3 GPs fulfilling the replacement requirement (more details in section on Respondents below). This yields a sample of respondents drawn from 16 districts, 64 blocks from within those districts, and 254 Gram Panchayats (GPs) from within those blocks.

Citizen Survey: The citizen survey was aimed at respondents from 16 households residing in each GP area. The survey firm was provided with a list of respondents (with replacements) drawn randomly from the electoral rolls available of all voting-age adults in Bihar's population. The target sample size is thus 4064 citizens (16 each from 254 GPs). Within the category of citizens, the survey additionally targeted office-bearing members of women's Self Help Groups (SHG) under a rural livelihoods program in Bihar known as Jeevika. However, we had no lists available with names of SHG leaders of the village-level organziations across GPs. In the absence of these lists, we relied on the survey firm to ensure that enumerator teams would identify SHG leaders during their field-work. The data from SHG leaders that has been provided to us is thus subject to a greater than usual caveat: the risk of whether the enumerator teams accuratelyidentified and obtained interviews with the targeted SHG respondents. The instructions provided to the survey teams was to ask the GP Mukhiya and other GPlevel respondents (such as the ANM, ASHA and AWW) about the GP-level federated organzation of all the SHGs across the GP's communities to identify its President,Secretary and Treasurer. That is, 3 SHG leaders were targeted for each GP, for a total sample of 762 (3 each from 254 GPs) SHG leaders.

Politician Survey: Lists were provided to the survey teams of all incumbent Mukhiyas to be interveiwed, and a random selection (with replacement) of 3 Ward members and 3 candidates from among those who contested the previous GP elections of 2016. The targeted sample size of GP politicians is thus 1778 (7 each from 254 GPs)

Bureaucrats: The survey firm was responsible for identifying and interviewing the respondents holding these positions. The final data submitted by the survey firm contains 293 respondents in supervisory or management positions, including: 13 Civil Surgeons,11 Chief Medical Officers (including 4 who were in Acting capacity), 23 Superintendents (including 13 in Deputy or Acting capacity), 9 District Programme Officers- NHM, 4 District RCH and Immunization In-charge, 7 District Community Mobilizers, 58 MOICs, 58 Acting Facility Incharge, 43 Block Program Managers-NHM, 29 Block RCH Programme officers, and 35 Block Community Mobilizers.

Public Providers of Health Services: The survey team was provided a list (with replacements) of 3 AWW workers to interveiw per GP, for a targeted sample of 762 AWW respondents. The survey team was provided with a list of randomly selected candidates for the categories of respondents for all the PHCs and higher-level health facilities (such as District Hospitals) across the 64 blocks of the study area.

Sampling deviation

Block Level: The survey firm was responsible for identifying the block-level politicians targeted to be interviewed. The targeted sample size of Block-Panchayat (Panchayat Samiti) elected members’ is 128 respondents (2 each from 64 blocks). The 57 MLAs across the 64 blocks of the study area were also identified by the survey firm. However, because of problems of reaching politicians at a time that was close to the 2019 elections in India, the survey firm was able to complete interviews with only 39 MLAs (of the targeted 57) , and with 119 Panchayat Samiti members (of the targeted 128).

District Level: The survey firm was responsible for identifying the MPs from constituencies within the 16 study districts, and the 32 respondents of the District-Panchayat (Zilla Parishad). Again, because of problems reaching political leaders at election time, the survey firm was able to interviewonly 9 MPs, and 28 Zilla Parishad members.

Public Providers of Health Care Services: The survey team was provided with a list of randomly selected candidates for the categories of respondents for all the PHCs and higher-level health facilities (such as District Hospitals) across the 64 blocks of the study area. However, the survey team reports substantial difficulty in adhering to this list because the personnel were not found at the health facilities. The survey team was not able to reach a random sample of providers appointed at these positions.