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Record W2832363064 · doi:10.3390/ijerph15071461

The Relationship of Safety with Burnout for Mobile Health Employees

2018· article· en· W2832363064 on OpenAlexafffundabout
Michael P. Leiter, Lois Jackson, Ivy Lynn Bourgeault, Sheri Price, Audrey Kruisselbrink, Pauline Gardiner Barber, Shiva Nourpanah

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2018
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsWilfrid Laurier UniversityAcadia UniversityUniversity of OttawaDalhousie University
FundersSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health Research
KeywordsCynicismBurnoutWorkloadOccupational safety and healthPsychologyHuman factors and ergonomicsApplied psychologySupervisorConfidence intervalPoison controlMedicineEnvironmental healthClinical psychologyManagementPolitical science

Abstract

fetched live from OpenAlex

OBJECTIVE: The study examined the relationship of occupational safety with job burnout. DESIGN: The study used a cross-sectional survey design. SETTING: The setting was Nova Scotia, Canada. PARTICIPANTS: = 156) completed surveys on road safety, workload, burnout and supervisor incivility. MAIN OUTCOME MEASURE: The main outcome measure was the Maslach Burnout Inventory. RESULTS: Results found that safety concerns improved the prediction of exhaustion beyond that provided by workload concerns alone. Further, confidence in safety buffered the relationship of exhaustion with cynicism such that the exhaustion/cynicism relationship was stronger for employees who had lower confidence in road safety. CONCLUSIONS: Employees' confidence in occupational safety while addressing work responsibilities on the road has implications for their experience of job burnout.

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)0.009
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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.100
GPT teacher head0.477
Teacher spread0.377 · 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

Citations3
Published2018
Admission routes3
Has abstractyes

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