Bibliographic record
Abstract
Patient handling through lifting and moving of patients or residents, especially in a long-term care setting, is a key risk factor leading to injuries in healthcare workers.1–3 These injuries cause enormous pain and suffering to the healthcare workers and their families, as well as impose an enormous economic burden on society through productivity losses, and increase in healthcare costs and workers compensation costs. Many of these patient handling injuries can be prevented through appropriate use of engineering control interventions, for example, through the introduction of overhead lift equipment, in long-term care facilities.4 The paper by Tompa et al 5 uses an analytic approach to evaluate a peer coaching programme for an overhead lift use intervention in the long-term care sector in British Columbia, Canada, by assessing its impact on reduction of injuries for healthcare workers (related to patient handling). It also performs a cost–benefit analysis (CBA) by estimating the concomitant increase in overall costs for the implementation of the peer coaching programme and the monetary value of the averted costs generated. This analysis is a very meaningful contribution to the field of occupational health literature because it addresses several critical issues that can help in reducing the barriers to occupational safety and health (OSH) interventions, and in enhancing the health and safety …
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".