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Record W2279643485 · doi:10.1080/19338244.2016.1154002

Anxiety and depression predict musculoskeletal disorders in health care workers

2016· article· en· W2279643485 on OpenAlexfundno aff
Pablo E. Romo, Rafael E. de la Hoz, José Miguel Villamor, Ignacio Mahíllo

Bibliographic record

VenueArchives of Environmental & Occupational Health · 2016
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
FundersUniversity of Saskatchewan
KeywordsMedicineAnxietyDepression (economics)MorningIncidence (geometry)Logistic regressionOccupational safety and healthHospital Anxiety and Depression ScalePhysical therapyHealth careShift workPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Incidence of musculoskeletal disorders (MSDs) is high among health care workers (HCWs). To determine whether MSDs are associated with preexisting anxiety and/or depression, a case-control study was carried out in female HCWs (56 cases/55 controls). Cases were HCWs with a first-time clinical diagnosis of MSD within the previous 2 years. Occupation, workplace, work shift, direct patient assistance, and anxiety/depression scores (Goldberg scale) were assessed. Increased risk of incident MSDs (multivariate logistic regression) was found in workers with preexisting anxiety/depression compared to those without (OR 5.01; 95% CI 2.20-12.05; p < .01). Other significant risk factors were direct patient assistance (OR 2.59; 95% CI 1.03-6.92; p = .04) and morning work shift (OR 2.47; 95% CI 0.99-6.48; p = .05). Preexisting anxiety/depression was associated with incident MSDs in HCWs, adjusting for occupational exposure risk factors.

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)0.000
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 score0.293
Threshold uncertainty score0.389

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.005
GPT teacher head0.283
Teacher spread0.278 · 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

Citations43
Published2016
Admission routes1
Has abstractyes

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