Key issues in human resource planning for home support workers in Canada
Bibliographic record
Abstract
OBJECTIVE: This paper is a synthesis of research on recruitment and retention challenges for home support workers (HSWs) in Canada. PARTICIPANTS: Home support workers (HSWs) provide needed support with personal care and daily activities to older persons living in the community. METHODS: Literature (peer reviewed, government, and non-government documents) published in the past decade was collected from systematic data base searches between January and September 2009, and yielded over 100 references relevant to home care human resources for older Canadians. RESULTS: Four key human resource issues affecting HSWs were identified: compensation, education and training, quality assurance, and working conditions. To increase the workforce and retain skilled employees, employers can tailor their marketing strategies to specific groups, make improvements in work environment, and learn about what workers value and what attracts them to home support work. CONCLUSIONS: Understanding these HR issues for HSWs will improve recruitment and retention strategies for this workforce by helping agencies to target their limited resources. Given the projected increase in demand for these workers, preparations need to begin now and consider long-term strategies involving multiple policy areas, such as health and social care, employment, education, and immigration.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".