MétaCan
Menu
Back to cohort
Record W1580454502 · doi:10.4000/pistes.2513

Self-assessment of stress in the workplace: a misleading health indicator

2010· article· fr· W1580454502 on OpenAlexvenueaboutno aff
Michel Vézina, Renée Bourbonnais, Alain Marchand, Robert Arcand

Bibliographic record

VenuePerspectives interdisciplinaires sur le travail et la santé · 2010
Typearticle
Languagefr
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthAutonomyPsychosocialPsychologyWork (physics)Stress (linguistics)Work stressDimension (graph theory)Environmental healthApplied psychologyMedicinePsychiatryPolitical science

Abstract

fetched live from OpenAlex

The purpose of this study is to document the relationships between some psychosocial constraints in the workplace and some mental health problems based on data for Québec from Cycle 1.2 of the Canadian Community Health Survey. From 2002 to 2005, results indicate a significant increase in weak social support from 45% to 49%, while for the same period, the number of persons indicating being stressed at work decreased significantly, dropping from 42% to 38%. However, while less autonomy in the workplace is associated with less stress at work, this dimension is also recognized as pathogenic for mental health. Therefore, self-assessment of stress in the workplace appears to be a misleading health indicator, since it fails to assess dimensions recognized as pathogenic for mental health.

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)0.007
metaresearch head score (Gemma)0.014
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 score0.057
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.013
GPT teacher head0.394
Teacher spread0.382 · 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

Citations0
Published2010
Admission routes2
Has abstractyes

Explore more

Same venuePerspectives interdisciplinaires sur le travail et la santéSame topicWorkplace Health and Well-beingFrench-language works237,207