A collaborative early intervention model using community physiotherapists to support early, safe return to work for healthcare workers.
Bibliographic record
Abstract
Workplace injuries can result in substantial financial loses to employers through disability insurance premiums, worker's compensation premiums and worker replacement costs. The implementation and integration of workplace injury prevention programs, supportive recovery resources, and early and safe return to work for injured and disabled workers are essential components of workplace disability management practices. Access to supportive resources such as physiotherapy in conjunction with modified work or transitional duties programs has shown to be effective in facilitating return to work for temporarily and permanently disabled worker ...This thesis examines the PEARS Plus program, a hybrid of an existing model which uses physiotherapy services, to assist in achieving positive return to work outcomes. PEARS Plus encompasses many of the principles and features of its on-site predecessor, PEARS, however it accesses community physiotherapy groups to provide physiotherapy services through a formalized relationship between the employer, the physiotherapy providers and the insurer, WorkSafeBC. Although this concept of Disability Management is not new, the practice of collaborating amongst all stake holders early within the claims process is. Furthermore, the use of allied healthcare professionals that do not co-exist with the workplace and act as a supportive resource during the early stages of the claim has been minimally researched and the effectiveness of this intervention in collaboration with the employer and insurer is somewhat unknown. --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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".