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Record W2059392021 · doi:10.1093/bjsw/bcu153

Workplace Congruence and Occupational Outcomes among Social Service Workers

2015· article· en· W2059392021 on OpenAlexaffabout
John R. Graham, Micheal L. Shier, David Nicholas

Bibliographic record

VenueThe British Journal of Social Work · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSocial workOccupational stressPsychologySocial WelfareJob satisfactionGovernment (linguistics)ProductivityWelfareCongruence (geometry)Occupational safety and healthSocial psychologyMedicinePolitical scienceEconomic growthEconomics

Abstract

fetched live from OpenAlex

Workplace expectations reflect an important consideration in employee experience. A higher prevalence of workplace congruence between worker and employer expectations has been associated with higher levels of productivity and overall workplace satisfaction across multiple occupational groups. Little research has investigated the relationship between workplace congruence and occupational health outcomes among social service workers. This study sought to better understand the extent to which occupational congruence contributes to occupational outcomes by surveying unionised social service workers (n = 674) employed with the Government of Alberta, Canada. Multiple regression analysis shows that greater congruence between workplace and worker expectations around workloads, workplace values and the quality of the work environment significantly: (i) decreases symptoms related to distress and secondary traumatic stress; (ii) decreases intentions to leave; and (iii) increases overall life satisfaction. The findings provide some evidence of areas within the workplace of large government run social welfare programmes that can be better aligned to worker expectations to improve occupational outcomes among social service workers.

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)0.001
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 score0.208
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0060.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.046
GPT teacher head0.354
Teacher spread0.307 · 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

Citations20
Published2015
Admission routes2
Has abstractyes

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