A cost-benefit analysis of peer coaching for overhead lift use in the long-term care sector in Canada
Bibliographic record
Abstract
OBJECTIVES: To evaluate whether a peer-coaching programme for patient lift use in British Columbia, Canada, was effective and cost-beneficial. METHODS: We used monthly panel data from 15 long-term care facilities from 2004 to 2011 to estimate the number of patient-handling injuries averted by the peer-coaching programme using a generalised estimating equation model. Facilities that had not yet introduced the programme served as concurrent controls. Accepted lost-time claim counts related to patient handling were the outcome of interest with a denominator of full-time equivalents of nursing staff. A cost-benefit approach was used to estimate the net monetary gains at the system level. RESULTS: The coaching programme was found to be associated with a reduction in the injury rate of 34% during the programme and 56% after the programme concluded with an estimated 62 lost-time injury claims averted. 2 other factors were associated with changes in injury rates: larger facilities had a lower injury rate, and the more care hours per bed the lower the injury rate. We calculated monetary benefits to the system of $748 431 and costs of $894 000 (both in 2006 Canadian dollars) with a benefit-to-cost ratio of 0.84. The benefit-to-cost ratio was -0.05 in the worst case scenario and 2.31 in the best case scenario. The largest cost item was peer coaches' time. A simulation of the programme continuing for 5 years with the same coaching intensity would result in a benefit-to-cost ratio of 0.63. CONCLUSIONS: A peer-coaching programme to increase effective use of overhead lifts prevented additional patient-handling injuries but added modest incremental cost to the system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".