NEGOTIATING CARE IN NURSING HOMES: THE EXPERIENCES OF FAMILY MEMBERS, RESIDENTS AND STAFF
Bibliographic record
Abstract
In Canada, families will be contributing upwards of 60 million hours of care per year in nursing homes by 2018. Even though families are a cornerstone of care in this sector, their involvement, both with their relative and the broader functioning of the nursing home, is largely invisible. Past research has indicated that families often experience role ambiguity, perceive conflict with staff, and may feel excluded from the care of their relative. In this critical ethnographic study, we aimed to examine the negotiation of care among families, residents and staff, particularly around decision-making and ‘hands-on’ care within the broader institutional environment. The study is taking place at two homes in British Columbia, Canada. A purposive sample of 26 family members, 17 staff members, and 8 residents participated in in-depth interviews. Additional participants were included in 145 hours of participant observation. Key findings illustrate ways in which individuals are working towards the similar care goals of ‘balancing risk and safety’, ‘navigating the boundaries of care practices, and ‘supporting person-centered care’. However, individual’s approaches to these care goals can be different, contributing to conflict among those providing and receiving care. These findings have implications for care processes that support effective communication and relational approaches to care in nursing homes.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.016 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".