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Record W2158248201 · doi:10.1080/00223980209604158

Police Officer Job Satisfaction in Relation to Mood, Well-Being, and Alcohol Consumption

2002· article· en· W2158248201 on OpenAlexaff
Andrea Kohan, Brian P. O’Connor

Bibliographic record

VenueThe Journal of Psychology · 2002
Typearticle
Languageen
FieldPsychology
TopicGrit, Self-Efficacy, and Motivation
Canadian institutionsLakehead University
Fundersnot available
KeywordsPsychologyOfficerJob satisfactionMoodRelation (database)Alcohol consumptionSocial psychologyConsumption (sociology)Applied psychologyClinical psychologyAlcoholSociologyPolitical science

Abstract

fetched live from OpenAlex

The authors examined job satisfaction, job stress, and thoughts of quitting in relation to positive and negative affect, life satisfaction, self-esteem, and alcohol consumption among police officers. Exploratory and confirmatory factor analyses revealed that 2 dimensions, positive affect and negative affect, provided a clear family-tree organizational framework for representing the otherwise confusing pattern of associations between job and well-being variables. Job satisfaction was primarily associated with positive affect, life satisfaction, and self-esteem; job stress was primarily associated with negative affect and alcohol consumption; thoughts of quitting had moderate loadings on both factors. The 2-dimensional framework may prove useful as a guide in reviewing research in this field and in selecting constructs and measures for inclusion in future research.

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)0.005
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.350
Teacher spread0.306 · 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

Citations126
Published2002
Admission routes1
Has abstractyes

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