767 <i>Healthy enterprise standard</i>(hes) evaluation: analysing effects and cost-benefit results with a qualitative evaluation of implementation process
Bibliographic record
Abstract
Introduction The Healthy Enterprise Standard (HES) is related to a certification program in Québec (Canada) and targets four areas: Lifestyle, Work-life balance, Workplace environment and Management practices. The aim of this study was to open the black box of intervention and analyse the implementation process in order to interpret effects of HES on health and workplace risk factors, and cost-benefit results from the employer’s perspective. Methods We used a before-after design for a two-case analysis with a mixed-method approach. In two organisations from different sectors, quantitative data were collected with a questionnaire among all active workers before the standard’s implementation (T1 organisation A=186, organisation B=1081) and 25–31 months after (T2 A=190, B=975). Psychosocial work factors (demand-control-support and effort-reward imbalance validated scales), psychological distress (validated Kessler-6), and work-related musculoskeletal problems (WMSP, 4 items from the Nordic Questionnaire) were measured as well as intervention exposure. Intervention costs data, presenteeism and absences data were collected. Qualitative data through interviews and focus groups in both organisations were recorded, transcribed and coded in order to perform thematic analysis a posteriori. Results The prevalence of psychosocial work factors (low social support, low reward) at T2 was lower amongst participants exposed to intervention in the Management practices area in both organisations. WMSP was lower for those exposed to the Workplace environment area in B. The average cost per worker per year was very similar for A and B whereas distribution of cost categories differed. The net benefit was highly positive in B and negative in A. Implementation analysis showed that each area of HES was associated to different types of facilitators and obstacles. Cyclical factors, communication and management involvement differed between A and B. Discussion These results show that the implementation process analysis provides interesting insights into understanding effect and cost-benefit results and improving OSH interventions.
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 | 0.065 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".