How Does the Presence of High Need for Recovery Affect the Association Between Perceived High Chronic Exposure to Stressful Work Demands and Work Productivity Loss?
Bibliographic record
Abstract
OBJECTIVE: Employers have increasingly been interested in decreasing work stress. However, little attention has been given to recovery from the exertion experienced during work. This paper addresses the question: how does the presence of high need for recovery (HNFR) affect the association between perceived high chronic exposure to stressful work demands (PHCE) and work productivity loss (WPL)?. METHODS: Data were from a population-based survey of 2219 Ontario workers. The Work Limitations Questionnaire was used to measure WPL. The relationship between HNFR and WPL was examined using four multiple regression models. RESULTS: Our results indicate that HNFR affects the association between PHCE and WPL. They also suggest that PHCE alone significantly increases the risk of WPL. CONCLUSION: Our results suggest that HNFR as well as PHCE could be an important factor for workplaces to target to increase worker productivity.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".