MétaCan
Menu
Back to cohort
Record W2156462378

Health status, preventive behaviour and risk factors among female nurses.

2009· article· en· W2156462378 on OpenAlexaffabout
Pamela A. Ratner, Richard Sawatzky

Bibliographic record

VenuePubMed · 2009
Typearticle
Languageen
FieldPsychology
TopicSleep and Work-Related Fatigue
Canadian institutionsTrinity Western UniversityUniversity of British Columbia
Fundersnot available
KeywordsSocioeconomic statusMedicineConfoundingLogistic regressionEnvironmental healthAlcohol consumptionMarital statusDemographyGerontologyPopulationAlcohol
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: This study compares the health status, preventive behaviour and risk factors of female nurses with those of other employed postsecondary-educated women. DATA AND METHODS; Cross-sectional data from the 2003 Canadian Community Health Survey were analyzed. Multiple logistic regression analyses were conducted to adjust for potential confounding by demographic and socio-economic characteristics. RESULTS: When confounding by demographic and socioeconomic characteristics was taken into account, nurses were more likely than other employed postsecondary-educated women to report back problems, that most work days were "quite a bit" or "extremely" stressful, and having had flu immunizations and cervical cancer screening. They were less likely to report insufficient consumption of vegetables and fruit or heavy alcohol use. INTERPRETATION: Canadian nurses' occupation may account for their higher prevalence of back problems and work stress. At the same time, their occupation may motivate flu immunization, cervical cancer screening, and vegetable and fruit consumption. Some problematic aspects of nurses' health profile are similar to those of other educated women.

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 score0.334
Threshold uncertainty score0.613

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 head0.020
GPT teacher head0.286
Teacher spread0.266 · 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

Citations46
Published2009
Admission routes2
Has abstractyes

Explore more

Same venuePubMedSame topicSleep and Work-Related FatigueFrench-language works237,207