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Record W2081647346 · doi:10.1037/a0037756

Sometimes it hurts when supervisors don’t listen: The antecedents and consequences of safety voice among young workers.

2014· article· en· W2081647346 on OpenAlexafffund
Sean Tucker, Nick Turner

Bibliographic record

VenueJournal of Occupational Health Psychology · 2014
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsUniversity of ManitobaUniversity of Regina
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsOpenness to experiencePsycINFOPsychologySupervisorOccupational safety and healthEmployee voiceHuman factors and ergonomicsApplied psychologyInjury preventionWork (physics)Poison controlSuicide preventionSocial psychologyMEDLINEMedicineManagementMedical emergencyEngineering

Abstract

fetched live from OpenAlex

We examined the relationship among having ideas about how to improve occupational safety, speaking up about them (safety voice), and future work-related injuries. One hundred fifty-five employed teenagers completed 3 surveys with a 1-month lag between each survey. We found that participants who were more likely to have ideas about how to improve occupational safety and had high affective commitment to the organization reported the highest level of safety voice. In turn, supervisor openness to voice moderated the relationship between safety voice and future work-related injuries. Specifically, future work-related injuries were most frequent when high levels of safety voice were combined with low supervisor openness to voice. The tested model clarifies the conditions under which workers share safety-related ideas with a supervisor and the real consequences of speaking up about them. We discuss the implications of these findings for safety management. (PsycINFO Database Record (c) 2014 APA, all rights reserved).

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)0.009
metaresearch head score (Gemma)0.002
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 score0.122
Threshold uncertainty score0.960

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.119
GPT teacher head0.505
Teacher spread0.386 · 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

Citations72
Published2014
Admission routes2
Has abstractyes

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