The need for a social revolution in residential care
Bibliographic record
Abstract
Loneliness and depression are serious mental health concerns across the spectrum of residential care, from nursing homes to assisted and retirement living. Psychosocial care provided to residents to address these concerns is typically based on a long-standing tradition of 'light' social events, such as games, trips, and social gatherings, planned and implemented by staff. Although these activities provide enjoyment for some, loneliness and depression persist and the lack of resident input perpetuates the stereotype of residents as passive recipients of care. Residents continue to report lack of meaning in their lives, limited opportunities for contribution and frustration with paternalistic communication with staff. Those living with dementia face additional discrimination resulting in a range of unmet needs including lack of autonomy and belonging-both of which are linked with interpersonal violence. Research suggests, however, that programs fostering engagement and peer support provide opportunities for residents to be socially productive and to develop a valued social identity. The purpose of this paper is to offer a re-conceptualization of current practices. We argue that residents represent a largely untapped resource in our attempts to advance the quality of psychosocial care. We propose overturning practices that focus on entertainment and distraction by introducing a new approach that centers on resident contributions and peer support. We offer a model-Resident Engagement and Peer Support (REAP)-for designing interventions that advance residents' social identity, enhance reciprocal relationships and increase social productivity. This model has the potential to revolutionize current psychosocial practice by moving from resident care to resident engagement.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".