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Record W4399660298 · doi:10.1016/j.ssaho.2024.100963

A qualitative system review: To lead public policy advancement for workplace psychological violence injury

2024· article· en· W4399660298 on OpenAlexaffabout
Wendy Bigcharles-Gaucher

Bibliographic record

VenueSocial Sciences & Humanities Open · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsRoyal Roads University
Fundersnot available
KeywordsLead (geology)Qualitative researchWorkplace violencePsychologyPublic policyForensic engineeringPolitical scienceMedicineInjury preventionMedical emergencyPoison controlEngineeringSociologySocial science

Abstract

fetched live from OpenAlex

This article presents a qualitative system analysis informing public policy advancement for workplace psychological violence injury. It is a unique look into how a jurisdiction responds to the injury caused by workplace psychological violence (workplace bullying, harassment, and discrimination campaigns). First ever provincial legislation addressing this harm was passed into law. This study found that the legislation has yet to be implemented into the medical response systems dealing with this workplace injury. Severe hardships and consequences are experienced by workers in attempting recovery. Some never get there. The Canadian Institute for Workplace Bullying Resources, a specialist with this injury, received this study's recommendations for system change to assist in closing gaps and reduce the harm experienced by workers in Alberta, Canada. This study found some causal factors to these system barriers to be common globally. Methods to map and lead advocacy system change through qualitative research for this workplace violence and caused injury are provided herein. This is a call to researchers and professionals globally who deal with both workplace psychological violence and injury, to use and build on this study's methods, tools, and outcomes to lead further needed system change.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.912
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0050.001
Scholarly communication0.0020.001
Open science0.0020.000
Research integrity0.0000.000
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 head0.242
GPT teacher head0.542
Teacher spread0.300 · 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 designTheoretical or conceptual
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

Citations0
Published2024
Admission routes2
Has abstractyes

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