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Record W2734316012 · doi:10.1093/geroni/igx004.2590

PROMISES AND PERILS OF PERMANENT RESIDENT ASSIGNMENT IN RESIDENTIAL CARE FACILITIES

2017· article· en· W2734316012 on OpenAlexaff
Sienna Caspar

Bibliographic record

VenueInnovation in Aging · 2017
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsStaffingTeamworkNursingWork (physics)MedicinePsychologyBusinessPolitical science

Abstract

fetched live from OpenAlex

Purpose: Permanent resident assignment (PRA) is the practice of assigning resident care aides (RCAs) to care for the same residents every shift they work. It has been touted as “the magic bullet” of culture change in residential care facilities (RCFs) and is considered by many to be essential to person-centred care. The purpose of this study was to explore how staff assignment practices affect the care giving experience from the perspectives of RCAs, residents, and family members. Methods: We conducted an institutional ethnography to explore the social organization of care in RCFs. The study was set in three RCFs: one with consistent PRA; one with PRA in one area of the facility and six week staffing rotations in another area of the facility; and one that had recently switched from PRA to three month staffing rotations. Data included 104 hours of naturalistic observation and 76 in-depth interviews. Results: The RCAs and residents described the primary benefit of PRA as being able to “get to know” each other well. Family members indicated that it assisted them in knowing who to go to when they had questions or concerns. However, RCAs also indicated that PRA had a negative impact on team work and diminished the exchange of individualized resident-care information amongst the care staff. Implications: Management initiatives are needed to ensure that the implementation of PRA does not result in the unintended consequence of diminishing staff members’ experience of teamwork or their ability and willingness to exchange pertinent, individualized resident-care information.

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)0.065
metaresearch head score (Gemma)0.120
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 score0.065
Threshold uncertainty score0.345

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0650.120
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0210.020
Scholarly communication0.0070.010
Open science0.0050.011
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0030.000

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 head0.407
Teacher spread0.348 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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