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Record W3089525102 · doi:10.1080/19338244.2020.1828244

Job strain and incident cardiovascular disease: the confounding and mediating effects of lifestyle habits. An overview of systematic reviews

2020· article· en· W3089525102 on OpenAlexafffund
Camille Riopel, Mathilde Lavigne‐Robichaud, Xavier Trudel, Alain Milot, Mahée Gilbert‐Ouimet, Denis Talbot, Karine Aubé, Chantal Brisson

Bibliographic record

VenueArchives of Environmental & Occupational Health · 2020
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsUniversité du Québec à RimouskiUniversité LavalThe Quebec Population Health Research Network
FundersCanadian Institutes of Health Research
KeywordsConfoundingMedicineJob strainDiseaseIncidence (geometry)DemographyGerontologyEnvironmental healthInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

This overview of systematic reviews (SR) aims to determine how the potential confounding and/or mediating effects of lifestyle habits were taken into consideration in SR examining the job strain effect on cardiovascular disease (CVD) incidence. Thirteen SR were identified. Lifestyle habits were often considered as confounders (n = 8). Authors reported that the job strain and CVD association remained after adjusting for lifestyle habits (n = 6); discussed lifestyle habits as potential mediators (n = 8) and postulated a total effect underestimation due to over-adjustment (n = 4). None investigated the magnitude of this bias.The effect of job strain on CVD is independent of lifestyle habits, supporting its causal effect on CVD. Lifestyle habits are also potential mediators. The current practice of adjusting for lifestyle habits can therefore lead to an underestimation of the total effect. Research using meditation methods is needed.

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)0.013
metaresearch head score (Gemma)0.071
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.071
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0060.008
Bibliometrics0.0110.011
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.065
GPT teacher head0.386
Teacher spread0.321 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations7
Published2020
Admission routes2
Has abstractyes

Explore more

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