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Record W1527114035 · doi:10.7175/fe.v13i4.272

Job satisfaction, workplace stress, unhealthy lifestyle choices, and productivity among Canadian nurses: an empirical study

2012· article· en· W1527114035 on OpenAlexaboutno aff
Karen J. Buhr

Bibliographic record

VenueFarmeconomia Health economics and therapeutic pathways · 2012
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsnot available
Fundersnot available
KeywordsProductivityJob satisfactionOrdered probitPsychologyProbit modelJob attitudeAffect (linguistics)Occupational stressApplied psychologySocial psychologyDemographic economicsJob performanceEconometricsEconomics

Abstract

fetched live from OpenAlex

BACKGROUND: Nurses’ occupational stress and job satisfaction can have an affect on lifestyle choices and productivity. OBJECTIVES: The objective of this study is to provide a detailed examination of the relationship between job satisfaction, job stress, unhealthy lifestyle choices, and productivity among Canadian nurses. METHODS: This study uses data from the confidential master data files of the 2005 National Survey of the Work and Health of Nurses (NSWHN). Ordinary least squares regressions and binary probit regression models were used to estimate the relationships between job satisfaction and job stress on productivity and unhealthy lifestyle choices. RESULTS: Workplace stress variables have a small effect on lifestyle choices. Job satisfaction has an effect on the probability of smoking, but not on drinking. Workplace stress and job satisfaction do not have statistically significant effects on productivity. DISCUSSION: The study found weak relationships among the work related stress variables and productivity. These findings can allow policy makers to consider efforts to reduce workplace stress which can be beneficial to productivity.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.024
Threshold uncertainty score0.173

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0040.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.044
GPT teacher head0.355
Teacher spread0.311 · 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

Citations2
Published2012
Admission routes1
Has abstractyes

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