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Record W2046200105 · doi:10.1002/ajim.20104

Health care restructuring, work environment, and health of nurses

2004· article· en· W2046200105 on OpenAlexafffundabout
Renée Bourbonnais, Chantal Brisson, Romaine Malenfant, Michel Vézina

Bibliographic record

VenueAmerican Journal of Industrial Medicine · 2004
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsCommission Scolaire des Hautes RivièresUniversité Laval
FundersMinistère de la Santé et des Services sociaux
KeywordsPsychosocialMedicinePsychological interventionRestructuringJob satisfactionHealth careCross-sectional studySocial supportJob strainOccupational medicineNursingEnvironmental healthFamily medicinePsychiatryPsychologyOccupational exposureSocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND: In the last 15 years, the health care system has undergone significant restructuring. The study's objective was to examine the psychosocial work environment and the health of nurses after major restructuring in comparison with two reference populations. METHODS: This cross-sectional study involved 2,006 nurses from 16 health centers. A questionnaire measured current work characteristics: psychological demands, decision latitude, and social support at work from Karasek's Job Content Questionnaire, organizational changes, and health effects. Prevalence ratios and binomial regression were used to examine the associations between current work characteristics, changes and psychological distress (PSI). RESULTS: There was a considerable increase in the prevalence of PSI and of adverse psychosocial work factors in comparison to the prevalence reported by a comparable group of nurses in 1994. These adverse factors were also more prevalent among nurses than among Québec working women and they were independently associated with psychological distress. CONCLUSION: Workplace interventions should be based on elements identified by many nurses as being problematic.

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score0.645

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.047
GPT teacher head0.397
Teacher spread0.350 · 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 designOther design
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

Citations77
Published2004
Admission routes3
Has abstractyes

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