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Record W2147625757 · doi:10.1177/0733464812446866

The Development and Evaluation of Mutual Support Groups in Long-Term Care Homes

2012· article· en· W2147625757 on OpenAlexaffabout
Kristine A. Theurer, Andrew Wister, Andrew Sixsmith, Habib Chaudhury, Loren D. Lovegreen

Bibliographic record

VenueJournal of Applied Gerontology · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsSimon Fraser UniversityUniversity of British Columbia
Fundersnot available
KeywordsLonelinessFocus groupPsychologyLong-term careCoping (psychology)Developmental psychologyGerontologySupport groupQualitative researchNursingClinical psychologySocial psychologyMedicinePsychiatrySociology

Abstract

fetched live from OpenAlex

This article describes the development of a new mutual support group intervention for long-term care homes (LTCH); evaluates the processes, structure, and content of the intervention; and addresses replication and sustainability. Tom Kitwood's model of personhood is used as the basis for developing a weekly discussion group using themes chosen by participants and theme-associated music, readings, and photographs. A mixed-methods qualitative process evaluation design encompasses focus groups, systematic observation of six resident groups, individual resident interviews (N = 65), and staff interviews (N = 7) in three LTCH in British Columbia, Canada. Resident reports and observations indicate positive benefits including a decrease in loneliness, the development of friendships, and increased coping skills, understanding, and support. Participating staff reported numerous benefits and described how the unique group structure fosters active participation of residents with moderate-severe cognitive impairment. This preliminary study suggests that mutual support groups have potential to offset loneliness, helplessness, and depression within LTCH.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.037
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.193

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.044
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0020.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.059
GPT teacher head0.386
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations42
Published2012
Admission routes2
Has abstractyes

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