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Record W2747276619 · doi:10.1177/1048291117724561

The Challenges of Mobilizing Workers on Gender Issues: Lessons from Two Studies on the Occupational Health of Teachers in Québec: Les défis de mobiliser les travailleur.ses sur la question du genre: Constats issus de deux recherches portant sur la santé au travail d'enseignant.es du Québec

2017· article· en· W2747276619 on OpenAlexaffabout
Jessica Riel, Marie-Ève Major

Bibliographic record

VenueNEW SOLUTIONS A Journal of Environmental and Occupational Health Policy · 2017
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsUniversité de SherbrookeUniversité du Québec en Outaouais
Fundersnot available
KeywordsGeneral partnershipDenialVocational educationPsychologyFeelingParticipatory action researchResistance (ecology)Work (physics)SociologyPedagogyMedical educationPolitical scienceSocial psychologyMedicineEngineering

Abstract

fetched live from OpenAlex

In partnership with the largest teachers' union confederation in Québec ( Centrale des syndicats du Québec), two qualitative research studies that integrate gender were conducted in the teaching community. These ergonomic studies advocated a participatory approach involving interviews and observations with high school teachers (thirty-five) and female teachers responsible for vocational training in trades with predominantly male clientele (twelve). The results revealed that gender influences work success by reducing the operational leeway available for female teachers to carry out their work and protect their health. In both studies, it was difficult to discuss these results with the teachers, especially with female teachers. Resistance, even denial, was present among high school teachers, while, in vocational training, resignation and a feeling of helplessness were observed. These reactions demonstrate that taking gender into account in prevention poses particular challenges that need to be addressed in order to promote equality between women and men in workplaces.

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)0.010
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.221
GPT teacher head0.493
Teacher spread0.271 · 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

Citations6
Published2017
Admission routes2
Has abstractyes

Explore more

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