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Record W7132883850

The role of work stress as a moderating variable in the chronic pain-depression relationship

2005· dissertation· W7132883850 on OpenAlexaboutno aff
Sarah Elizabeth Patricia Munce

Bibliographic record

VenueTSpace · 2005
Typedissertation
Language
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsnot available
Fundersnot available
KeywordsChronic painDepression (economics)Chronic stressModerationChronic depressionDiscretionSample (material)Chronic disease
DOInot available

Abstract

fetched live from OpenAlex

This Master's thesis examined the role of work stress and sex differences in the chronic pain-depression association. Using the Canadian Community Health Survey (CCHS) Cycle 1.1, of 78,593 working individuals, 8% reported having major depression versus 12% in the sample with chronic pain. Both depression and comorbid chronic pain and depression were twice as prevalent in women than in men. Unexpectedly, overall work stress, decision authority, or psychological demands did not moderate the chronic pain and depression association. There was a trend towards poor skill discretion as a moderating variable of chronic pain and depression. No sex effects were observed for any of the domains of work stress as a moderating variable in the chronic pain-depression link. In terms of the etiology and management of depression, the impact of work stress alone and the potential role of poor skill discretion in those individuals with comorbid chronic pain, should be considered.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.024
GPT teacher head0.396
Teacher spread0.371 · 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
Published2005
Admission routes1
Has abstractyes

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