Modifiable workplace risk factors contributing to workplace absence across health conditions: A stakeholder-centered best-evidence synthesis of systematic reviews
Bibliographic record
Abstract
BACKGROUND: A challenge facing stakeholders is the identification and translation of relevant high quality research to inform policy and practice. This study engaged academic and community stakeholders in conducting a best evidence-synthesis to enhance knowledge use. OBJECTIVES: To identify modifiable workplace disability risk and protective factors across common health conditions impacting work-related absence. METHODS: We searched MEDLINE, Embase, CINHAL, The Cochrane Library, PsycINFO, BusinessSourceComplete, and ABI/Inform from 2000 to 2011. Systematic reviews that employed quantitative, qualitative, or mixed methods of work-focused population were considered for inclusion. Two or more independent reviewers reviewed titles only, titles and abstracts, and/or full articles when assessing eligibility for inclusion. Selected articles underwent methodological screening. RESULTS: The search strategy, expert input and grey literature identified 2,467 unique records from which 142 full text articles underwent comprehensive review. Twenty-seven systematic reviews met eligibility criteria. Modifiable work factors found to have consistent evidence across two or more health conditions included lack of social support, increased physical demands at work, job strain, lack of supervisory support, increased psychological demands, low job satisfaction, low worker control of job, and poor leadership quality. CONCLUSIONS: The active engagement of stakeholders led to greater understanding of relevance of the study findings for community stakeholders and appreciation of the mutual benefits of collaboration.
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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 | 0.041 | 0.155 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".