“Nobody Can Be Equipped for This”: Advice From New Residents of Long‐Term Care Facilities
Bibliographic record
Abstract
BACKGROUND: The transition into a long-term care facility (LTCF) is difficult for older adults, prompting calls for clinicians to help guide and plan. Yet we know little about how those with lived experience of moving into an LTCF would advise others. METHODS: We conducted in-person semi-structured interviews with nursing home (NH) and assisted living (AL) residents within 6 months of moving into an urban non-profit continuing care retirement community in California between 2023 and 2024. Interviews were guided by theories of long-term care utilization and asked, "what advice would you give others considering an LTCF?" We thematically analyzed interviews using the constant comparative method. RESULTS: We interviewed 8 NH and 6 AL residents. Mean participant age was 82 (range 73-90); 8 were female, 1 participant was Asian, 13 participants were White, and mean Montreal Cognitive Assessment was 19 (range 12-25). Residents talked about LTCF entry within a broader phase of life defined by dependence following sudden unexpected health crises. Advice reflected strategies for this phase of life and highlighted challenges outside of their control. Some residents advised preparation by visiting facilities and budgeting time and resources to plan but discovered care arrangements did not work out as promised; care was fragmented, and dependence caused them to re-evaluate what they wanted. Some advised avoidance as they disliked living in an LTCF but had little control over entry, leading to distrust of those making decisions for them. Others advised acceptance and believed luck or fate dictated how everything worked out in the end. CONCLUSIONS: Unanticipated health crises catalyze entry into LTCFs. New residents advised others to prepare for, avoid, or accept LTCF entry, reflecting different strategies for approaching a unique phase of life and highlighting systemic problems that could be improved. Anticipatory guidance for LTCF transitions should acknowledge their sudden nature, these strategies, and the need for system reform.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".