Evaluating the RECOVER model as an effective early intervention progam [sic]
Bibliographic record
Abstract
This project report is part of an overall evaluation of the RECOVER pilot expansion. RECOVER is an example of an employer-driven early intervention initiative that relied on the development of collaborative working relationships between Fraser Health, WorkSafeBC, and community physiotherapy providers. The pilot's aim was to minimize lengthy delays to appropriate treatment, and to keep the injured workers connected to the workplace during their time of recovery. In this report, a population of eligible Fraser Health employees who experienced an acute, musculoskeletal injury while completing their duties at work was compared in terms of this population group's sample of eligible employees who voluntarily chose to accept the employer's offer to participate in the pilot versus those who voluntarily declined the employer's offer, even though they were eligible for participation. Variables for comparison included the employees' age, occupational group, work status, and WSBC SDL office managing their file. Qualitative instruments were also used to obtain mean satisfaction values from pilot participants and RECOVER service providers. Findings from this mixed-methods evaluation indicated that as of four months post-pilot expansion, RECOVER demonstrated that it was an effective way of delivering early intervention services to injured employees with an acute work-related musculoskeletal injury. This was observed through a high rate of voluntary employee acceptance for pilot participation, and through high mean satisfaction values received from RECOVER participants and service providers. --P. ii.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.007 | 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".