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Record W2767722604 · doi:10.1177/0733464817737622

Shining a Light: Examining Similarities and Differences in the Work Psychology of Health Support Workers Employed in Long-Term Care and Home and Community Care Settings

2017· article· en· W2767722604 on OpenAlexaff
Tyrone Perreira, Whitney Berta, Audrey Laporte, Liane Ginsburg, Raisa Deber, G. E. P. Elliott, Janet Lum

Bibliographic record

VenueJournal of Applied Gerontology · 2017
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsToronto Metropolitan UniversityYork UniversityUniversity of Toronto
Fundersnot available
KeywordsJob satisfactionPsychologyPsychological interventionOrganizational citizenship behaviorEmpowermentWork engagementDescriptive statisticsCitizenshipLong-term careNursingGerontologyWork (physics)Organizational commitmentSocial psychologyMedicinePolitical science

Abstract

fetched live from OpenAlex

Health Support Workers (HSWs) provide up to 80% of care to residents and clients in the long-term care (LTC) and home and community care (HCC) sectors but have received little research attention compared with the regulated professions. The authors explore similarities and differences in the work psychology of HSWs employed in LTC and HCC settings. Data were collected via survey from 276 LTC and 184 HCC HSWs. Descriptive statistics and path analyses were conducted. HSWs in LTC and HCC settings have significant, positive associations between organizational citizenship behaviors directed toward the organization (OCB-Os) and psychological empowerment, as well as intention to stay (ITS) and job satisfaction. For LTC sector HSWs, there are significant relationships between OCB-Os and quality of work life (QWL), ITS and work engagement, and individual performance and both job satisfaction and QWL. For the HCC sector, OCB-Os and ITS are significantly and directly related to organizational commitment. This study has implications for organizations interested in developing targeted interventions to improve the retention of HSWs.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.086
GPT teacher head0.416
Teacher spread0.330 · 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 teacher head, not a consensus.

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

Citations16
Published2017
Admission routes1
Has abstractyes

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