Challenges in Caring for Unbefriended Residents in Long-term Care Homes: A Qualitative Study
Bibliographic record
Abstract
OBJECTIVES: This study examined challenges experienced by long-term care staff in caring for unbefriended residents who are incapacitated and alone. These residents often are estranged from or have no living family or live geographically distant from them and require a public guardian as their surrogate decision-maker. To date, research on unbefriended older adults has focused on those living in acute care and community settings. Little is known about those living in long-term care homes. METHOD: We conducted semi-structured interviews with 39 long-term care staff (e.g., registered nurses, care aides, social workers) and 3 public guardians. Staff were sampled from seven long-term care homes in Alberta, Canada. We analyzed interview transcripts using content analysis and then using the theoretical framework of complex adaptive systems. RESULTS: Long-term care staff experience challenges unique to unbefriended residents. Guardians' responsibilities did not fulfill unbefriended residents' needs, such as shopping for personal items or accompanying residents to appointments. Consequently, the guardians rely on long-term care staff, particularly care aides, to provide increased levels of care and support. These additional responsibilities, and organizational messages dissuading staff from providing preferential care, diminish quality of work life for staff. DISCUSSION: Long-term care homes are complex adaptive systems. Within these systems, we found organizational barriers for long-term care staff providing care to unbefriended residents. These barriers may be modifiable and could improve the quality of care for unbefriended residents and quality of life of staff. Implications for practice include adjusting public guardian scope of work, improving team communication, and compensating staff for additional care.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".