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Record W1749012195 · doi:10.3233/wor-131652

A longitudinal and comparative study of psychological distress among professional workers in regulated occupations in Canada

2014· article· en· W1749012195 on OpenAlexaffabout
Nathalie Cadieux, Alain Marchand

Bibliographic record

VenueWork · 2014
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsWorkforcePsychological distressDistressOddsMultilevel modelLongitudinal studyPsychologyMental healthMedicineClinical psychologyDemographyLogistic regressionPsychiatrySociologyPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Although several studies are concerned by the phenomenon of psychological distress at work, few studies have looked at the prevalence of psychological distress among professional workers in the regulated occupations and compare this prevalence with other occupations. OBJECTIVES: This study propose to define regulated occupations by laying out the theoretical boundaries that apply to the practice of these occupations and try to understand how regulated occupations contributed to the experience of psychological distress in the Canadian workforce over time. METHOD: Multilevel logistical regression analyses on longitudinal data were performed to compare the odds of experiencing psychological distress over time among professional workers in regulated occupations (n=276) and among other professional workers, classified into 6 categories (n=6731), over a 12-year period. RESULTS: The results show that proportion of distress in the workforce decreases for all occupations between Cycle 1 and Cycle 7 of the NPHS, but this decrease is not linear over time. The results show also that regulated occupations present a lower probability of psychological distress only when compared with white-collar workers. CONCLUSIONS: These results suggest that occupation contributes little toward understanding the prevalence of psychological distress in the Canadian workforce. Further research needs are also discussed.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.472
Threshold uncertainty score0.515

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.064
GPT teacher head0.419
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 teacher head, 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

Citations1
Published2014
Admission routes2
Has abstractyes

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