Taking Account of Gender Differences When Designing Interventions in Occupational Health? Lessons from a Study of the “Healthy Enterprise” Standard in Québec: Les différences de genre sont-elles prises en compte lors de la conception des interventions de prévention en santé au travail? Résultats d une étude sur la norme “Entreprises en Santé” dans les entreprises au Québec
Bibliographic record
Abstract
The literature shows substantial differences in occupational exposures between men and women, both between and within occupations, but remains very sparse on whether interventions are tailored to gender differences in the workplace. Our objective was to determine whether gender differences are taken into account when designing prevention interventions. This study is part of a project on the evaluation of interventions implemented in the framework of the "Healthy Enterprise" standard in Quebec organizations. Three sets of quantitative and qualitative data were collected in seven organizations and triangulated. Our results show that in the process of elaborating and implementing activities, the main objectives were to reach a maximum number of workers and meet the needs identified in a health and risk diagnosis. Activities were not tailored to the needs of specific subgroups of employees, such as gender or age. Not distinguishing men's and women's situations in this diagnosis may play a role in intervention design. Résumé La littérature montre des différences d'exposition au travail importantes entre les hommes et les femmes, y compris à catégories d'emploi identiques. Les études sur l'adaptation des interventions de prévention aux différences de genre dans les milieux de travail sont quasiment inexistantes dans la littérature. Notre objectif était de déterminer dans quelle mesure les différences de genre sont prises en compte lors de la conception des interventions de prévention. Cette étude fait partie d'un projet d'évaluation des interventions mises en uvre dans le cadre de la norme «Entreprises en santéé au Québec. Trois séries de données quantitatives et qualitatives ont été collectées auprès de sept organisations et analysées par triangulation. Nos résultats montrent que lors du processus d'élaboration des activités, les principaux objectifs étaient d'atteindre un nombre maximal de travailleurs et de remplir les besoins identifiés dans le diagnostic des risques et de la santé des travailleurs. Le fait que la situation des hommes et des femmes n'ait pas été analysée séparément dans le diagnostic peut avoir joué un râle dans ce résultat. Les activités de prévention n'ont pas été conçues en tenant compte de sous-groupes de travailleurs en fonction du genre ou de l'âge.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.040 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".