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Record W4387115562 · doi:10.3928/00989134-20230918-02

Job Satisfaction for Caregivers and Other Employees in Innovative Long-Term Care Homes for Residents With Cognitive Problems

2023· article· en· W4387115562 on OpenAlexaffabout
M. Hardy, Philippe Voyer, Clémence Dallaire, Diane Morin, Pierre Durand, Edeltraut Kröger, Camille Savoie, Anne‐Marie Veillette

Bibliographic record

VenueJournal of Gerontological Nursing · 2023
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsAutonomyJob satisfactionFlexibility (engineering)PaceWork (physics)Gerontological nursingPsychologyNursingQuality (philosophy)CognitionDelegationLong-term careGerontologyMedicineSocial psychologyPolitical scienceManagement

Abstract

fetched live from OpenAlex

New housing models have emerged in Europe, Australia, the United States, and Canada. Intended for individuals with neurocognitive disorders, these models are characterized by a philosophy centered on the person, self-determination, liberty of choice, flexibility of care, acceptance of risk, and autonomy. Work and care are organized according to the pace and preferences of residents. The current multiple case study highlights the main sources of job satisfaction for caregivers and other employees in four innovative residential settings. Five themes are addressed as perceived by 58 employees: Work Motivation , Work Organization , Collaboration and Decision-Making Latitude , Quality of Work Life , and Continuing Education . These data will help inform clinical staff, policymakers, and the scientific community about clinical and organizational practices that contribute to job satisfaction in innovative residential settings. [ Journal of Gerontological Nursing, 49 (10), 36–43.]

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.079
GPT teacher head0.424
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

Citations0
Published2023
Admission routes2
Has abstractyes

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