Management practices that promote preventive measures compliance: A comparative analysis between hospital healthcare workers and teachers
Bibliographic record
Abstract
Frontline workers were at significant risk during the COVID-19 pandemic; as a result, several recommendations were implemented to protect the health and safety of frontline professions. Despite guidelines issued by the relevant authorities, several observations have emerged that workplaces do not always implement the wearing of personal protective equipment and compliance with other preventive measures. This study investigates healthcare workers and teachers’ compliance with these measures in Quebec, Canada. Specifically, this study aims to examine the application of preventive measures among hospital healthcare workers and elementary and high school teachers, identify the factors associated with the application of preventive measures in the context of the COVID-19 pandemic, and understand the differences in the dynamics of applying preventive measures in hospitals and educational institutions. This study is based on a proposed new model for preventive measure compliance, which classifies the factors that could influence the dependent variable of compliance with preventive measures into three variables: organizational context, organizational incentives, and individual social responsibility. Data were collected using an observational cross-sectional design through an online questionnaire survey of teachers and hospital healthcare workers in Canada. The results highlight that the three variables can impact the application of preventive practices; however, these variables did not intervene in the same manner in hospitals and educational institutions. Indeed, the results show that management practices differ in the two sectors of activity, with practices being more favorable for hospital healthcare workers.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".