Comparing Recruitment and Retention Strategies for Rehabilitation Professionals among Hospital and Home Care Employers
Bibliographic record
Abstract
PURPOSE: The objective of this study was to compare hospital and home care employers' rankings of both the importance and the feasibility of workforce strategies for recruiting and retaining rehabilitation professionals. METHODS: An online self-administered questionnaire was distributed to all employers of rehabilitation professionals in Ontario hospitals (n=144) and Community Care Access Centre home care providers (n=34). Importance and feasibility rankings were based on the percentage of high ratings; 95% CIs were used to determine significant differences between hospital and home care rankings of recruitment and retention strategies. RESULTS: The response rate was 50% (72/144) from hospitals and 73.5% (25/34) from home-care settings. The recruitment and retention strategies considered most important and feasible for rehabilitation therapists, regardless of setting, were 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 by hospitals than by home care providers. CONCLUSIONS: Similarities exist between hospital and home care employers in terms of the importance and feasibility of recruitment and retention strategies for rehabilitation professionals. However, when developing a rehabilitation health human resources plan, the strategies identified as different between hospital and home care settings should be taken into account.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".