Organizational measures to protect the mental health of healthcare and social services staff during COVID-19: perspectives of human resources advisors
Bibliographic record
Abstract
Purpose In the context of a larger study aiming to develop a workplace mental health support tool during the COVID-19 pandemic, this paper sought to document the measures targeting the psychosocial work environment that were introduced or maintained in Quebec’s health and social services network institutions, in Canada, and the perceived efficacy of the measures by human resources advisors. Design/methodology/approach This study is based on a descriptive research design using an online questionnaire administered between May 14 and June 4, 2021 to human resources advisors who were responsible for implementing such measures, and thus served as key informants. Findings On the basis of respondents from 31 participating institutions, it was found that measures focusing on interpersonal relations, flexible or reduced work time and access to protective equipment were most frequently reported as implemented and were amongst the measures deemed most efficacious, along with COVID-19 screening, financial compensation during isolation and facilitation of telework. Several staffing and worktime measures with the potential to directly target excessive workload during the pandemic were deemed less efficacious by these advisors. Originality/value This study proposes an alternative to avoid directly soliciting healthcare staff when they are not easily available. In addition to providing an overview of promising organizational measures that institutions can implement in times of crisis and beyond, this study contributes to the literature on intervention processes, by highlighting the possibility and added value of surveying key informants as a means of gaining insight into implementation through the lens of human resources advisors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".