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Record W2166267462 · doi:10.1093/geront/43.suppl_2.96

A Cooperative Communication Intervention for Nursing Home Staff and Family Members of Residents

2003· article· en· W2166267462 on OpenAlexaff
Karl Pillemer, J. Jill Suitor, Charles Henderson, Rhoda Meador, Leslie Schultz, Julie Robison, Carol Hegeman

Bibliographic record

VenueThe Gerontologist · 2003
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsHealth Care Foundation
FundersNational Institute on Aging
KeywordsNursingNursing homesIntervention (counseling)Interpersonal communicationMedicineNursing staffDementiaPsychologyFamily medicineSocial psychology

Abstract

fetched live from OpenAlex

PURPOSE: This article reports on a randomized, controlled study of Partners in Caregiving, an intervention designed to increase cooperation and effective communication between family members and nursing home staff. DESIGN AND METHODS: Participants included 932 relatives and 655 staff members recruited from 20 nursing homes, randomly assigned to treatment and control conditions. Parallel training sessions on communication and conflict resolution techniques were conducted with the family and staff in the treatment group, followed by a joint meeting with facility administrators. RESULTS: Positive outcomes were found for both family and staff members in the treatment group. Both groups showed improved attitudes toward each other, families of residents with dementia reported less conflict with staff, and staff reported a lower likelihood of quitting. IMPLICATIONS: Multiple studies report significant interpersonal stress between family members of nursing home residents and facility staff members. Partners in Caregiving appears to be an effective way to improve family-staff relationships in nursing homes.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.085
GPT teacher head0.431
Teacher spread0.345 · 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

Citations140
Published2003
Admission routes1
Has abstractyes

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