Employers, Home Support Workers and Elderly Clients: Identifying Key Issues in Delivery and Receipt of Home Support
Bibliographic record
Abstract
The Nexus Home Care Project examines the experiences of employers, home support workers and elderly clients and their family members in the delivery and receipt of home support services. The primary purpose of this research is to identify salient issues in the delivery and receipt of home support services to elderly individuals from the perspective of employers, home support workers and clients. The data for this study, funded by the Canadian Institutes of Health Research, are derived from in-depth interviews with home support employers (n=11), home support workers (n=32) and elderly clients (n=14) in British Columbia. Employers emphasized recruitment and retention and the increasing complexity of client needs, and raised questions regarding the appropriateness of home support as a part of the healthcare continuum. Home support workers stressed scheduling and time demands, the tension in providing intimate ongoing care at an emotional distance and the balance between tasks outlined in the care plan and the needs and wants of elderly clients. Elderly clients indicated an ongoing need to prepare for and manage services and expressed a need for companionship. Findings are discussed as they inform and extend our understanding of the key tensions in home support. Strategies for addressing these tensions are also identified.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".