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Record W4401067012 · doi:10.1016/j.jsr.2024.07.001

Assessing psychological distress of healthcare workers with and without work injuries: The role of job control

2024· article· en· W4401067012 on OpenAlexaff
Joshua S. Davis, Steve Granger, Nick Turner

Bibliographic record

VenueJournal of Safety Research · 2024
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsConcordia UniversityUniversity of Calgary
Fundersnot available
KeywordsOccupational safety and healthPsychological distressDistressControl (management)PsychologyJob controlHealth careApplied psychologyWork (physics)Human factors and ergonomicsInjury preventionPoison controlMedicineClinical psychologyMental healthMedical emergencyPsychiatryEngineeringComputer science

Abstract

fetched live from OpenAlex

INTRODUCTION: The study investigates the relationship between work-related injuries, psychological distress, and the influence of perceived job control on healthcare workers, using Bakker and Demerouti's (2007) job demands-resources model as theoretical grounding. METHOD: We analyzed data from 610 healthcare workers (81.1% female) at a northern UK hospital, incorporating both self-reported and organizationally recorded work injury incidents over the three years preceding the survey, along with measures of psychological distress and perceived job control. RESULTS: Unexpectedly, we found that the occurrence of work-related injuries, irrespective of the method of reporting, is not related to lower psychological distress among those employees who report a high level of job control. This relationship holds even when adjusting for various demographic and occupational variables. CONCLUSIONS AND PRACTICAL APPLICATIONS: Given the prevalence of work injuries in the healthcare sector, our findings suggest a need for a deeper exploration into how job characteristics might interact to offset the consequences of work injuries, challenging existing assumptions and opening new avenues for research into the psychology of workplace safety.

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)0.011
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
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 head0.134
GPT teacher head0.562
Teacher spread0.427 · 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.

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
Published2024
Admission routes1
Has abstractyes

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