Peer coaching and mentoring: A new model of educational intervention for safe patient handling in health care
Bibliographic record
Abstract
BACKGROUND: To reduce the risk of patient handling-related musculoskeletal injury, overhead ceiling lifts have been installed in health care facilities. To increase ceiling lift usage for a variety of patient handling tasks, a peer coaching and mentoring program was implemented among the direct care staff in the long-term care subsector in British Columbia, Canada. They received a 4-day training program on body mechanics, ergonomics, patient-handling techniques, ceiling lift usage, in addition to coaching skills. METHODS: A questionnaire was administered among staff before and after the intervention to evaluate the program's effectiveness. RESULTS: There were 403 and 200 respondents to the pre-intervention and post-intervention questionnaires. In general, staff perceived the peer-coaching program to be effective. The number of staff who reported to be using ceiling lifts "often and always" went higher from 64.5% to 80.5% (<0.001) after coaching program implementation. Furthermore, staff reported that they were using the ceiling lifts for more types of tasks post-intervention. Staff reported that the peer coaching program has increased their safety awareness at work and confidence in using the ceiling lifts. CONCLUSIONS: The findings suggest that this educational model can increase the uptake of mechanical interventions for occupational health and safety initiatives. It appears that the training led to a greater awareness of the availability of or increased perceptions of the number of ceiling lifts, presumably through coaches advocating their use.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".