Developing and Retaining Homecare Nurses Through Employer-Based Tuition Assistance Programs: A Mixed Methods Study
Bibliographic record
Abstract
PURPOSE: This study describes how an employer-based tuition-assistance program for homecare workers at one Canadian homecare organization enabled nursing career advancement and retention. DESIGN: A convergent parallel mixed-methods design. METHODS: We reviewed existing administrative data and concurrently conducted semi-structured interviews. Descriptive statistics were used on quantitative data and qualitative data was analyzed using thematic analysis. A joint data display was developed to integrate findings from both quantitative and qualitative data together. FINDINGS: Tuition assistance reduced financial barriers to career advancement; 83% of recipients remained with their employer for at least 1-year post-studies but only 29% experienced career advancement. Psychosocial supports, career navigation and coaching to ease the licensing and role transition processes were identified as opportunities to support learners. CONCLUSION: Employer-based tuition assistance programs are impactful in helping to develop skilled employees. Practical enhancements to further support career transitions may maximize retention to address urgent homecare staffing challenges. CLINICAL EVIDENCE: Employer-based tuition assistance can be a useful strategy to support nursing career growth and staff retention.
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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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".