Summary of work health and safety and return to work performance in 2021-22.

Only ** percent of remote workers in Poland were ready to return to the office in 2021. ** percent preferred to stay at home due to the arduous commute to the company's headquarters. ** percent were those who were accustomed to the home office mode. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Approximately ********* of companies in Moscow were planning to return some of their remote workers back to the office after the remote work requirement in the city had been lifted, according to a survey conducted in ************. A slightly smaller share of businesses were going to let their employees continue working remotely.

WHS Return to Work Performance Annual Report Data for SA Ambulance Service

A March 2021 survey revealed that roughly ** percent of consumers in Hong Kong expected to go back into their workplace in the following six months. On the other hand, for only ** percent of respondents, there was a slim chance that they would come back to their place of work in the next half year.

To identify factors associated with employment between six months and five years after traumatic brain injury (TBI). Using a predefined search algorithm, four electronic databases were searched for literature published between 2014 and the first half of April 2021 containing predictors of employment outcome. Data were selected in accordance with the PRISMA flow and the whole process was conducted by two reviewers who had to attain a consensus. The study results were discussed with an expert panel, in order to provide guidance for future research on this topic. This review found clear evidence for employment status at time of injury, occupation at time of injury, Glasgow Coma Scale, length of stay, disability level and primary payer to be predictors of return to work after TBI. More literature investigating in depth the functioning and environmental factors is required for further improvement of predictions, rehabilitation and policy.Implications for rehabilitationThis study identifies predictors of return to work in TBI patients, which can be used to identify patients with high risk early in the recovery process.Current literature shows difficulties with general functioning are a barrier for return to work, but gives no indication about effective therapeutic interventions.More knowledge about modifiable factors is desirable to improve rehabilitation and, thereby, employment outcomes after TBI. This study identifies predictors of return to work in TBI patients, which can be used to identify patients with high risk early in the recovery process. Current literature shows difficulties with general functioning are a barrier for return to work, but gives no indication about effective therapeutic interventions. More knowledge about modifiable factors is desirable to improve rehabilitation and, thereby, employment outcomes after TBI.

In 2021, ** percent of respondents from a global survey state they are comitted to remote working and will evolve operations to support a hybrid workforce in the future. Overall, ** percent of respondents generally prefer a return to office-based work environments, whereas ** percent of respondents are considering hybrid work arrangements.

The Employment data from the 2021 Federal Census covers labour force status, employment status, labour force participation rate, industry, and occupation. For questions, please contact socialresearch@calgary.ca. Please visit Data about Calgary's population for more information.

Labour force status refers to whether a person was employed, unemployed or not in the labour force during the reference period. Not in the labour force refers to persons who were neither employed nor unemployed during the reference period. This includes persons who, during the reference period were either unable to work or unavailable for work. It also includes persons who were without work and who had neither actively looked for work in the past four weeks nor had a job to start within four weeks of the reference period.

Employment status refers to the employment status of a person during the period of Sunday, May 2 to Saturday, May 8, 2021. An employed person is one who did any work at all at a job or business, that is, paid work in the context of an employer-employee relationship, or self-employment. This category excludes persons not at work because they were on layoff or between casual jobs, and those who did not then have a job (even if they had a job to start at a future date). While an unemployed person is one who was without paid work or without self-employment work and was available for work. An unemployed person either: had actively looked for paid work in the past four weeks; was on temporary lay-off and expected to return to his or her job; or had definite arrangements to start a new job in four weeks or less.

Labour force participation rate refers to the total labour force in that group, expressed as a percentage of the total population in that group.

Industry refers to the general nature of the business carried out in the establishment where the person worked. The industry data are produced according to the North American Industry Classification System (NAICS).

Occupation refers to the kind of work performed in a job, a job being all the tasks carried out by a particular worker to complete their duties. An occupation is a set of jobs that are sufficiently similar in work performed. The occupation data are produced according to the National Occupational Classification (NOC) 2021.

This is a one-time load of Statistics Canada federal census data from 2021 applied to the Communities, Wards, and City geographical boundaries current as of 2022 (so they will likely not match the current year's boundaries). Update frequency is every 5 years. Data Steward: Business Unit Community Strategies (Demographics and Evaluation). This dataset is for general public and internal City business groups.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

According to a survey among consumers in the Philippines in June 2021, ** percent of the respondents stated that they were likely to return to their workplace in the next six months. While other companies allowed work-from-home setup, Filipino workers were still more likely to go back to their workplace.

This study explored the usage of occupational therapy treatment with psychologically injured public safety personnel (PSP) from Ontario, Canada. We used a descriptive quantitative approach with summary data provided by the Workplace Safety and Insurance Board (WSIB) of Ontario documenting occupation therapy (OT) treatment of psychologically injured PSP who had an approved WSIB Mental Stress Injury Program (MSIP) claim between 2017 and 2021. Variables examined included demographics, career type, injury type, and return to work (RTW) outcomes. Chi-square Tests of Independence were used to compare differences between PSP who received OT treatment and those who did not. Analysis revealed that in the total cohort of 6674 approved PSP MSIP claims, 15% (n = 991) of PSP received OT treatment. Communicators (21%) and correctional workers (17%) were most likely to receive OT treatment while paramedics (13%) were less likely. PSP claimants who received OT treatment were more likely to have a cumulative event injury (71%) compared to the rest of the cohort (55%) and were more likely to not have started a RTW process (62%) compared to the rest of the cohort (43%). PSP who received OT treatment had more days away from work on average than those who did not (913 days vs. 384 days). This data reveals that PSP with cumulative injuries and higher lengths of time away from work more frequently received OT treatment as part of their WSIB MSIP claim; it is possible that this higher degree of claim complexity influenced their RTW outcomes. Worker’s compensation organizations should consider their health care decision-making processes to foster prompt access to treatment and proactive RTW pathways.

Most research focuses around impairments in body function and structure, with relatively only a small number exploring their social impact. 1) compare characteristics for individuals who before stroke were blue collar vs. white collar workers 2) identify clinical, functional, and job-related factors associated with return to work within 1 year after discharge 3) identify specific ADL individual items (assessed at rehabilitation discharge) as return to work predictors and 4) identify return to work causal mediators. Retrospective observational cohort study, analyzing adult patients with stroke admitted to rehabilitation between 2007 and 2021, including baseline Barthel Index (BI) and return to work assessments between 2008 and 2022. Kaplan–Meier survival curves and Cox proportional hazards were applied. Causal mediation analyses using 1000-bootstrapped simulations were performed. A total of 802 individuals were included (14.6% returned to work), 53.6% blue-collar and 46.4% white-collar. Blue-collar workers showed significantly higher proportion of ischemic stroke, diabetes, dyslipidemia, and hypertension. Individuals not returning to work presented a higher proportion of blue collar, dominant side affected, aphasia, lower BI scores, and larger length of stay (LOS). Multivariable Cox proportional hazards identified age at injury, aphasia, hypertension, and total discharge BI score (C-Index = 0.74). Univariable Cox models identified three independent BI items at all levels of independence: bathing (C-Index = 0.58), grooming (C-Index = 0.56) and feeding (C-Index = 0.59). BI efficiency (gain/LOS) was a causal mediator. Blue collar workers showed higher proportion of risk factors and comorbidities. Novel factors, predictors, and a return to work mediator were identified.

This 6MB download is a zip file containing 5 pdf documents and 2 xlsx spreadsheets. Presentation on COVID-19 and the potential impacts on employment

May 2020Waka Kotahi wants to better understand the potential implications of the COVID-19 downturn on the land transport system, particularly the potential impacts on regional economies and communities.

To do this, in May 2020 Waka Kotahi commissioned Martin Jenkins and Infometrics to consider the potential impacts of COVID-19 on New Zealand’s economy and demographics, as these are two key drivers of transport demand. In addition to providing a scan of national and international COVID-19 trends, the research involved modelling the economic impacts of three of the Treasury’s COVID-19 scenarios, to a regional scale, to help us understand where the impacts might be greatest.

Waka Kotahi studied this modelling by comparing the percentage difference in employment forecasts from the Treasury’s three COVID-19 scenarios compared to the business as usual scenario.

The source tables from the modelling (Tables 1-40), and the percentage difference in employment forecasts (Tables 41-43), are available as spreadsheets.

Arataki - potential impacts of COVID-19 Final Report

Employment modelling - interactive dashboard

The modelling produced employment forecasts for each region and district over three time periods – 2021, 2025 and 2031. In May 2020, the forecasts for 2021 carried greater certainty as they reflected the impacts of current events, such as border restrictions, reduction in international visitors and students etc. The 2025 and 2031 forecasts were less certain because of the potential for significant shifts in the socio-economic situation over the intervening years. While these later forecasts were useful in helping to understand the relative scale and duration of potential COVID-19 related impacts around the country, they needed to be treated with care recognising the higher levels of uncertainty.

The May 2020 research suggested that the ‘slow recovery scenario’ (Treasury’s scenario 5) was the most likely due to continuing high levels of uncertainty regarding global efforts to manage the pandemic (and the duration and scale of the resulting economic downturn).

The updates to Arataki V2 were framed around the ‘Slower Recovery Scenario’, as that scenario remained the most closely aligned with the unfolding impacts of COVID-19 in New Zealand and globally at that time.

Find out more about Arataki, our 10-year plan for the land transport system

May 2021The May 2021 update to employment modelling used to inform Arataki Version 2 is now available. Employment modelling dashboard - updated 2021Arataki used the May 2020 information to compare how various regions and industries might be impacted by COVID-19. Almost a year later, it is clear that New Zealand fared better than forecast in May 2020.Waka Kotahi therefore commissioned an update to the projections through a high-level review of:the original projections for 2020/21 against performancethe implications of the most recent global (eg International monetary fund world economic Outlook) and national economic forecasts (eg Treasury half year economic and fiscal update)The treasury updated its scenarios in its December half year fiscal and economic update (HYEFU) and these new scenarios have been used for the revised projections.Considerable uncertainty remains about the potential scale and duration of the COVID-19 downturn, for example with regards to the duration of border restrictions, update of immunisation programmes. The updated analysis provides us with additional information regarding which sectors and parts of the country are likely to be most impacted. We continue to monitor the situation and keep up to date with other cross-Government scenario development and COVID-19 related work. The updated modelling has produced employment forecasts for each region and district over three time periods - 2022, 2025, 2031.The 2022 forecasts carry greater certainty as they reflect the impacts of current events. The 2025 and 2031 forecasts are less certain because of the potential for significant shifts over that time.

Data reuse caveats: as per license.

Additionally, please read / use this data in conjunction with the Infometrics and Martin Jenkins reports, to understand the uncertainties and assumptions involved in modelling the potential impacts of COVID-19.

COVID-19’s effect on industry and regional economic outcomes for NZ Transport Agency [PDF 620 KB]

Data quality statement: while the modelling undertaken is high quality, it represents two point-in-time analyses undertaken during a period of considerable uncertainty. This uncertainty comes from several factors relating to the COVID-19 pandemic, including:

a lack of clarity about the size of the global downturn and how quickly the international economy might recover differing views about the ability of the New Zealand economy to bounce back from the significant job losses that are occurring and how much of a structural change in the economy is required the possibility of a further wave of COVID-19 cases within New Zealand that might require a return to Alert Levels 3 or 4.

While high levels of uncertainty remain around the scale of impacts from the pandemic, particularly in coming years, the modelling is useful in indicating the direction of travel and the relative scale of impacts in different parts of the country.

Data quality caveats: as noted above, there is considerable uncertainty about the potential scale and duration of the COVID-19 downturn. Please treat the specific results of the modelling carefully, particularly in the forecasts to later years (2025, 2031), given the potential for significant shifts in New Zealand's socio-economic situation before then.

As such, please use the modelling results as a guide to the potential scale of the impacts of the downturn in different locations, rather than as a precise assessment of impacts over the coming decade.

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The main purpose of this survey is to study the coverage of the employment insurance program. It provides a meaningful picture of who does or does not have access to EI benefits among the jobless and those in a situation of underemployment. The Employment Insurance Coverage Survey also covers access to maternity and parental benefits. The survey was designed to produce a series of precise measures to identify groups with low probability of receiving benefits, for instance, the long-term jobless, labour market entrants and students, people becoming unemployed after uninsured employment, people who have left jobs voluntarily and individuals who are eligible, given their employment history, but do not claim or otherwise receive benefits. The survey provides a detailed description of the characteristics of the last job held as well as reasons for not receiving benefits or for not claiming. Through the survey data, analysts will also be able to observe the characteristics and situation of people not covered by EI and of those who exhausted EI benefits, the job search intensity of the unemployed, expectation of recall to a job, and alternate sources of income and funds. Survey data pertaining to maternity and parental benefits answer questions on the proportion of mothers of an infant who received maternity and parental benefits, the reason why some mothers do not receive benefits and about sharing parental benefits with their spouse. The survey also allows looking at the timing and circumstances related to the return to work, the income adequacy of households with young children and more.

More than ** percent of companies in Poland expect to return to office work in 2021. One in **** companies hopes to return to the office during the vacation season or even earlier. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

The Census 2021 in a nutshell The Census 2021 is a snapshot of the population living in Belgium on 1 January 2021. It provides a wide range of figures on housing and demographic, socio-economic and educational characteristics of the citizens. The objective of the Census is twofold: to comply with the European regulation[1] and to produce statistics to address national specific needs (public services, international organizations, researchers, enterprises and private individuals). Previously based on an exhaustive survey of all citizens, since 2011 the Census has been based exclusively on the use of administrative databases. Definitions The various statistical units Population The population taken into account for the Census 2021 is the residential population, as registered in the National Register of Natural Persons (RNPP) on 1 January 2021. The Belgian population includes Belgians and non-Belgians who have been allowed or authorised to settle or to stay in Belgium but does not include non-Belgians living on the territory for less than three months, asylum seekers and non-Belgians in an illegal situation[2]. Private households This group includes people living alone in a dwelling and groups of several people living in the same dwelling and providing themselves with essentials for living. Family nuclei A family nucleus is defined as two or more persons who belong to the same household and who are related as husband and wife, as partners in a registered partnership, as partners in a consensual union, or as parent and child. Living quarters Living quarters refer to all quarters used as the usual residence of one or several persons. Conventional dwellings Conventional dwellings are separate units (surrounded by walls and covered by a roof) that are independent (with a direct access from the street or a staircase, passage) and designed to be used as a permanent dwelling. Occupied conventional dwellings Occupied conventional dwellings are conventional dwellings used as the usual residence of one or several private households. Variables and their description Sex This variable is used to distinguish men from women. Age The age reached in completed years of age on 1 January 2021. Place of usual residence The place of residence is that registered in the National Register on 1 January 2021. So this is the place of legal residence. The Belgian municipalities have changed between 2011 and 2021. In the comparisons shown on this website, the figures for 2011 are broken down according to the municipalities of 2021. Locality A locality is defined as a distinct population cluster, that is an area defined by population living in neighbouring or contiguous buildings. This area constitutes a group of buildings, none of which is separated from its nearest neighbour by more than 200 meters. The Belgian municipalities have changed between 2011 and 2021. In the comparisons shown on this website, the figures for 2011 are broken down according to the municipalities of 2021. Situation on the labour market The situation on the labour market gives information on the economic activity of the population (employed, unemployed and inactive persons) during the last week of the year 2020. Employed persons The following persons are considered as employed : persons aged 15 or over and who either performed at least one hour of work in the last week of the year for pay or profit, in cash or in kind; or were temporarily absent during the reference period from a job to which they maintained a formal attachment . Unemployed The unemployed comprise: persons aged 15 years or over who were: without work , that is, were not in wage employment or self-employment during the reference week; and currently available for work , that is, were available for wage employment or self-employment during the reference week and for two weeks after that; and actively seeking work , that is, had taken specific steps to seek wage employment or self-employment within four weeks ending with the reference week. Pension or capital income recipients Recipients of pension or capital income should be assigned to the Pension or capital income recipients category only if they have attained the national minimum age for economic activity ( 15 years or over ); and are outside of the labour force, and receive a pension and/or capital income. Even if a formal link with their enterprise is maintained, people in early retirement are considered as retired, as they do not intend to return to work. Students Students comprise all persons attending school who: have attained the national minimum age for economic activity ( 15 years or over ); and are outside of the labour force, and are not recipients of a pension or of capital income. Labour force The “labour force” comprises all persons who fulfil the requirements for inclusion among the employed or the unemployed during the reference week. Employed population The employed population includes all employed persons during the reference week.

This spreadsheet contains information about the Social Services Workforce collected from services regulated by the Care Inspectorate (via its Annual Returns in December) and from the annual December Local Authority Social Work Services Staffing Return in 2021. We created this spreadsheet to provide you with more detailed information about the workforce than is currently available in the Workforce Data Report.