Quality of working life indicators in Canadian health care organizations: a tool for healthy, health care workplaces?
Bibliographic record
Abstract
BACKGROUND: Quality-of-work-life (QWL) includes broad aspects of the work environment that affect employee learning and health. Canadian health care organizations (HCOs) are being encouraged to monitor QWL, expanding existing occupational health surveillance capacities. AIM: To investigate the understanding, collection, diffusion and use of QWL indicators in Canadian HCOs. METHODS: We obtained cooperation from six diverse public HCOs managing 41 sites. We reviewed documentation relevant to QWL and conducted 58 focus groups/team interviews with strategic, support and programme teams. Group interviews were taped, reviewed and analysed for themes using qualitative data techniques. Indicators were classified by purpose and HCO level. RESULTS: QWL indicators, as such, were relatively new to most HCOs yet the data managed by human resource and occupational health and safety support teams were highly relevant to monitoring of employee well-being (119 of 209 mentioned indicators), e.g. sickness absence. Monitoring of working conditions (62/209) was also important, e.g. indicators of employee workload. Uncommon were indicators of biomechanical and psychosocial hazards at work, despite their being important causes of morbidity among HCO employees. Although imprecision in the definition of QWL indicators, limited links with other HCO performance measures and inadequate HCO resources for implementation were common, most HCOs cited ways in which QWL indicators had influenced planning and evaluation of prevention efforts. CONCLUSIONS: Increase in targeted HCO resources, inclusion of other QWL indicators and greater integration with HCO management systems could all improve HCO decision-makers' access to information relevant to employee health.
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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 | 0.014 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".