Using the Accreditation Canada Quality Worklife revalidated Model to predict healthy work environments
Bibliographic record
Abstract
Background The relevance of improving quality worklife in implementing healthy workplaces successfully has been demonstrated by both the Canadian Health Services Research Foundation and Accreditation Canada. In partnership with Accreditation Canada, this article focuses on two issues: the relationships between quality worklife and healthy work environment, and the prediction of healthy work environments using Accreditation Canada’s revalidated Quality Worklife Model. Methods Using the 2008 and 2010 de-identified worklife data gathered among staff in organisations participating in the Qmentum accreditation program from all Canadian provinces (9,578 French speaking and 16,398 English-speaking respondents), this article attempts to demonstrate how the Quality Worklife revalidated Model predicts healthy work environments. The revalidation of the Quality Worklife Model was done using first principal component factor analysis (FA) with direct oblimin rotations (using SPSS 16.0), followed by a confirmatory factor analyses (using LISREL 8.80) on the French and the English samples. Furthermore, multivariate analyses of variance were conducted in order to detect mean differences between the different work environment groups linked to the psychological and physical consequences. Results The results suggest that the healthy work environment group is associated with high work adjustment, good physical and mental health as well as low absenteeism and health-related presenteeism. On the other hand, the results suggest that the poor work environment group and to a less significant extent the subthreshold work environment is associated with low work adjustment, poor physical and mental health and high absenteeism and health-related presenteeism. Conclusion The proposed model suggests that by categorising the Quality Worklife scores in three work environment groups based on a sample set of 11 quality worklife items, it becomes possible to predict employees’ risk of having poor work adjustment, poor mental and physical health, and poor work-related behaviours.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".