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Record W2011777828 · doi:10.1097/jom.0b013e31829fd065

Receiving Workplace Mental Health Accommodations and the Outcome of Mental Disorders in Employees With a Depressive and/or Anxiety Disorder

2013· article· en· W2011777828 on OpenAlexafffundabout
Carmelle Bolo, Jitender Sareen, Scott B. Patten, Norbert Schmitz, Shawn R. Currie, Jianli Wang

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2013
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsAnxietyMoodMental healthPsychiatryOdds ratioMood disordersPrevalence of mental disordersLogistic regressionPopulationClinical psychologyMajor depressive disorderAnxiety disorderPsychologyConfidence intervalDepression (economics)MedicineAccommodationInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVES: To investigate the association between receiving workplace accommodations and the 1-year risk of mood/anxiety disorders. METHODS: A general population sample of employees in Alberta, Canada, with a prior or current mental disorder (N = 715) was observed for 1 year. Mental disorders were determined on the basis of the Diagnostic and Statistical Manual, 4th revision, criteria. RESULTS: In participants who needed but did not receive any accommodations, 30.8% had a mood/anxiety disorder 1 year later. Receiving needed accommodations was associated with a lowered risk of 24.5%. Logistic regression showed that the percentage of having accommodation needs met was significantly associated with the risk of a mental disorder 1 year later (odds ratio = 0.27; 95% confidence interval = 0.11 to 0.65). CONCLUSIONS: Receiving needed accommodations was associated with better outcomes for mental disorders. Studies are needed to confirm the effectiveness of specific accommodations for enhancing the prognosis of mood/anxiety disorders.

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 score0.024
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

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

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 head0.019
GPT teacher head0.346
Teacher spread0.327 · 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

Citations32
Published2013
Admission routes3
Has abstractyes

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