Family Matters: The Work and Skills of Family/Friend Carers in Long-Term Residential Care
Bibliographic record
Abstract
The unpaid care work undertaken by family members and friends often continues when relatives move to long-term residential care (LTRC). Using a feminist political economy approach, this paper explores the labour and skills of family/friend carers—most of whom are women—in LTRC. Data were gathered using the rapid site-switching ethnography method, which involved document analysis, qualitative interviews with 25 family members, and observations in eight LTRC facilities across Canada. We present five themes, developed through a thematic analysis of interviews and observations, to give insight into the labour and skills of these unpaid carers in LTRC: maintaining relationships, navigating the system to assert residents’ needs, supplementing care, assisting other residents, and working for change. In our analysis, we tease out complexities between family/friend care work in practice and the descriptions of their involvement in resident and family handbooks—guiding documents that serve as touchstones for communication between LTRC facilities and families. We note discrepancies between the impoverished descriptions of family engagement in handbooks and the complex labour undertaken by many family/friend carers in LTRC. These discrepancies reinforce the invisibility of unpaid care and an undervaluing of the skills involved in this labour. To conclude, we suggest an addition to handbooks that could serve to better recognize the involvement of family members and friends in LTRC.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".