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Record W4312103284 · doi:10.1093/geroni/igac059.1241

ORGANIZATIONAL CONTEXT'S EFFECT ON CARE AIDES' PSYCHOLOGICAL EMPOWERMENT IN WESTERN CANADA

2022· article· en· W4312103284 on OpenAlexaffabout
Alba Iaconi, Yinfei Duan, Yuting Song, Matthias Hoben, Leslie A. Hayduk, Peter Norton, Carole A. Estabrooks

Bibliographic record

VenueInnovation in Aging · 2022
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsEmpowermentCompetence (human resources)PsychologyContext (archaeology)NursingSocial capitalSocial psychologyMedicineSociologyPolitical scienceGeography

Abstract

fetched live from OpenAlex

Abstract This quantitative cross-sectional sub project investigated the effects of organizational context and individual characteristics on psychological empowerment of care aides working in nursing homes. We analyzed data collected from 3765 care aides from 91 nursing homes across Western Canada between 09/2019 and 03/2020. From the random-intercept mixed effects regression models we identified significant predictors at different levels for each component of psychological empowerment. At the organizational outer context level: region and home ownership model. At the inner context (care unit) level: formal interactions (β=-0.07, p=0.03; competence), evaluation (β=0.20, p<0.02; self-determination), culture (β=0.20, p<0.02; self-determination), communication (β=0.16, p<0.001; self-determination), and social capital (β=0.22, p=0.01; impact). At the individual level: care aides’ sex, language and job satisfaction.These findings suggest important ways in which contextual elements may influence staff quality of work life characteristics and underscore the need to consider context operating at different levels, as well as consider individual and contextual interaction.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.025
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0020.000
Open science0.0010.003
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 head0.028
GPT teacher head0.382
Teacher spread0.355 · 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
Published2022
Admission routes2
Has abstractyes

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