603 Workplace practices and policies to prevent msd: developing an implementation guide
Bibliographic record
Abstract
Introduction Musculoskeletal disorders (MSD) continue to be a major burden for workplaces and workers as well as insurance and health systems. Evidence-based approaches are desired but research-to-practice gaps remain. One reason for gaps is the necessary research of sufficient quality is often not available. However evidence-based practice considers both scientific evidence as well as practitioner expertise. Our objective is to synthesise evidence from the scientific literature, practice evidence (policies and practices), and experiences from stakeholders. Methods Scientific evidence from recently published reviews, including a recent review our team completed, will be synthesised. Evidence from practitioners’ expertise and worker experiences are being collected using a web-based survey, focus groups, and interviews with representatives from various stakeholder groups from multiple sectors. We are using the Public Health Agency of Canada’s best practices portal to structure data collection of workplace practices and policies. We are synthesising the evidence gathered from stakeholders with that from recently published systematic reviews. Result Recent systematic review results revealed 61 high and medium studies addressing MSD. The studies described 30 different intervention categories. There was strong evidence that resistance training has a positive effect and moderate evidence that stretching, using a feedback mouse, and workstation forearm supports have positive effects. However the level of evidence was too low to make recommendations for many other interventions. The survey and interview/focus groups to collect practice-based evidence are ongoing. Discussion The presentation will focus on current policies and practices described by our practitioner and workplace audiences as compared to the scientific evidence. The discussion will outline the synthesis of evidence and co-creation (with OHS stakeholders) of a practical guide to help workplaces develop and implement effective practices and policies to prevent MSD and help workers with MSD return to work safely.
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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 | 0.037 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.033 | 0.019 |
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".