Bibliographic record
Abstract
The purpose of this thesis is to identify important and feasible recruitment and retention strategies for rehabilitation professionals in hospital and home care settings, to ultimately assist in the development of a rehabilitation health human resource (HHR) plan relevant to the Ontario setting. The study methodology included a modified-delphi process using two expert panels and an online survey distributed to all employers of rehabilitation professionals in Ontario hospitals (n=144) and Community Care Access Centre home care providers (n=34). Key recruitment and retention strategies that were identified for rehabilitation therapists regardless of setting included: communication between employer and worker, compensation packages, access to research, and professional development in budget planning. Tangible resources, support personnel, work safety and marketing rehabilitation careers to high school students were ranked significantly higher among hospitals than home rare The three main areas of focus for HHR planning in the rehabilitation sector should include strategies addressing Quality of Worklife and Work Environment, Financial Incentives and Marketing and Professional Development.
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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 | 0.062 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".