Types and patterns of safety concerns in home care: client and family caregiver perspectives
Bibliographic record
Abstract
OBJECTIVE: Drawing on interviews with home care clients and their family caregivers, we sought to understand how these individuals conceptualize safety in the provision and receipt of home care, how they promote safety in the home space and how their safety concerns differ from those of home support workers. DESIGN: In-depth, semi-structured interviews were conducted with clients and family caregivers. The analysis included topic and analytical coding of participants' verbatim accounts. SETTING: Interviews were completed in British Columbia, Canada. PARTICIPANTS: Totally 82 clients and 55 caregivers participated. RESULTS: Clients and family caregivers identified three types of safety concerns: physical, spatial and interpersonal. These concerns are largely multi-dimensional and intersectional. We present a conceptual model of client and caregiver safety concerns. We also examine the factors that intensify and mitigate safety concerns in the home. CONCLUSIONS: In spite of safety concerns, clients and family caregivers overwhelmingly prefer to receive care in the home setting. Spatial and physical concerns are the most salient. The financial burden of creating a safe care space should not be the client's alone to bear. The conceptualization and promotion of safety in home care must recognize the roles, responsibilities and perspectives of all of the actors involved, including workers, clients and their caregivers.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".