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Record W2105945333 · doi:10.1177/0018726706067595

Social structures, agent personality and workers' mental health:A longitudinal analysis of the specific role of occupation and of workplace constraints-resources on psychological distress in the Canadian workforce

2006· article· en· W2105945333 on OpenAlexaffabout
Alain Marchand, Andrée Demers, Pierre Durand

Bibliographic record

VenueHuman Relations · 2006
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPsychologyWorkforceMental healthPersonalityMultilevel modelDistressSocial supportSocial psychologyClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

This study examines the role of occupations and work conditions in psychological distress with a model of social action in which psychological distress results from stress created by the constraintsresources of structures of daily life, macrosocial structures, and agent personality. Using longitudinal data from 6611 workers nested in 471 occupations, multilevel regression analyses confirm the model. Occupations account for 1.6 percent of the variation. Social support and job insecurity contribute to distress, but greater decision authority increases distress. Skill utilization follows a J curve. Family structure, social network outside the workplace, and the personality of the agent do not moderate the influence of the workplace, with the sole exception of strained marital relations. The findings support the hypothesis that occupations and certain workplace constraintsresources contribute independently to psychological distress. Researchers in workplace mental health must expand their theoretical perspectives to avoid erroneous conclusions about the specific role of the workplace.

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)0.003
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 score0.068
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.073
GPT teacher head0.404
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 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

Citations66
Published2006
Admission routes2
Has abstractyes

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