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Record W2901468008 · doi:10.1136/oemed-2018-105426

Bruised apples: violence against women in the education sector

2018· letter· en· W2901468008 on OpenAlexaboutno aff
Jennifer Taylor

Bibliographic record

VenueOccupational and Environmental Medicine · 2018
Typeletter
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsnot available
Fundersnot available
KeywordsOccupational safety and healthHealth careWorkplace violencePsychological interventionMedicineTertiary sector of the economyWork (physics)Poison controlSuicide preventionNursingBusinessPolitical scienceEnvironmental healthEngineeringLawMarketing

Abstract

fetched live from OpenAlex

Chen et al 1 illuminate the increasing risk of workplace violence to women in the education sector through analysis of lost time claims and emergency department visits in Ontario, Canada. The authors compare workplace violence lost-time injury workers’ compensation claims as reported to the Workplace Safety & Insurance Board from 2002 to 2015 across industries, specifically focusing on healthcare that traditionally has high rates and education that has been understudied. Unlike education, workplace violence in the healthcare sector has received much attention. For example, in the USA, the National Institute for Occupational Safety and Health created resources for nurses,2 home health aides3 and workplace violence training and policy considerations.4 Chen et al 1 found that while the rate of violence to women in healthcare declined, the education sector unfortunately went in the opposite direction. This is a sounding of the alarm that another sector of human service work—and one predominated by women—is experiencing risk. Perhaps the education sector can benefit from interventions similar to those healthcare. It is …

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.247
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.018
GPT teacher head0.276
Teacher spread0.258 · 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 designNot applicable
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 routes1
Has abstractyes

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