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Record W2317198586 · doi:10.7870/cjcmh-2006-0024

Intervention Research on Work Organization and Health: Research Design and Preliminary Results on Mental Health

2006· article· en· W2317198586 on OpenAlexafffundvenue
Chantal Brisson, Viviane Cantin, Brigitte Larocque, Michel Vézina, Alain Vinet, Louis Trudel, Renée Bourbonnais

Bibliographic record

VenueCanadian Journal of Community Mental Health · 2006
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsUniversité LavalCentre de Santé et de Services Sociaux de la Vieille-CapitaleCentre hospitalier universitaire de Québec
FundersNational Institute for Occupational Safety and HealthCanadian Institutes of Health ResearchCenters for Disease Control and Prevention
KeywordsIntervention (counseling)Mental healthPsychologyWork (physics)Applied psychologyDistressResearch designPublic healthClinical psychologyMedicinePsychiatryNursingEngineeringSociology

Abstract

fetched live from OpenAlex

This article presents the overall research design and preliminary results of an intervention study on work organization and health which integrates the 3 phases of intervention research: development, implementation, and effectiveness. The demand-latitude-support and effort-reward-imbalance models were used to assess adverse work organization factors. Psychological distress was measured using the Psychiatric Symptoms Index. The intervention development phase in a major department of a public organization revealed an excess of psychological demands, job strain, low reward, effort-reward imbalance, and psychological distress compared to reference populations, and allowed workers to identify 5 priorities for action. The implementation phase showed that changes that were put into effect were consistent with those priorities.

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)0.069
metaresearch head score (Gemma)0.057
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.069
Threshold uncertainty score0.363

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0690.057
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.003
Science and technology studies0.0030.003
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.296
GPT teacher head0.513
Teacher spread0.217 · 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

Citations24
Published2006
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Community Mental HealthSame topicWorkplace Health and Well-beingFrench-language works237,207