Filling In: Family Member Support for Nonrelative Residents in Long-Term Care Homes
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Past research about family involvement in long-term care (LTC) homes mainly focuses on family members' involvement with their own relative, interactions with staff, and collective activities such as Family Councils. Our research provides novel insights into family member's involvement in the care of residents who are not their relatives, an area that has not previously been explored. RESEARCH DESIGN AND METHODS: This critical ethnographic study examined ways that family members negotiate and navigate their roles within LTC homes. Data collection and analysis took place at 3 LTC homes in British Columbia, Canada, between 2014 and 2018. Data were collected through participant observation and semistructured interviews. Eleven family member participants shared experiences of caring for residents who were not their relatives. RESULTS: The umbrella theme was "filling in," which takes place in a care environment that is understaffed and underresourced. The subthemes reflect the various ways that families are "filling in": responding to resident's needs, supporting staff to respond to resident needs, and filling in for residents' families. DISCUSSION AND IMPLICATIONS: Caring for residents who are not their relatives is facet of family involvement in LTC homes that has not been previously explored. Many family members have expertise in providing person-centered care and they extend this expertise to residents who are not their relatives. Policies and legislation are needed to formalize family involvement in caring for nonrelative residents as it is a component of quality of care for all residents.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".