The evolving role of health care aides in the long-term care and home and community care sectors in Canada
Bibliographic record
Abstract
Health Care Aides (HCAs) provide up to 80% of the direct care to older Canadians living in long-term care facilities, or in their homes. They are an understudied workforce, and calls for health human resources strategies relating to these workers are, we feel, precipitous. First, we need a better understanding of the nature and scope of their work, and of the factors that shape it. Here, we discuss the evolving role of HCAs and the factors that impact how and where they work. The work of HCAs includes role-required behaviors, an increasing array of delegated acts, and extra-role behaviors like emotional support. Role boundaries, particularly instances where some workers over-invest in care beyond expected levels, are identified as one of the biggest concerns among employers of HCAs in the current cost-containment environment. A number of factors significantly impact what these workers do and where they work, including market-level differences, job mobility, and work structure. In Canada, entry into this 'profession' is increasingly constrained to the Home and Community Care sector, while market-level and work structure differences constrain job mobility to transitions of only the most experienced workers, to the long-term care sector. We note that this is in direct opposition to recent policy initiatives designed to encourage aging at home. Work structure influences what these workers do, and how they work; many HCAs work for three or four different agencies in order to sustain themselves and their families. Expectations with regard to HCA preparation have changed over the past decade in Canada, and training is emerging as a high priority health human resource issue. An increasing emphasis on improving quality of care and measuring performance, and on integrated team-based care delivery, has considerable implications for worker training. New models of care delivery foreshadow a need for management and leadership expertise--these workers have not historically been prepared for leadership roles. We conclude with a brief discussion of the next steps necessary to generating evidence necessary to informing a health human resource strategy relating to the provision of care to older Canadians.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.021 | 0.006 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".