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Record W4402960918 · doi:10.1080/13691058.2024.2408349

‘How can you worry about employment and survival at the same time?’: employment and mental health among precariously employed cisgender and transgender sexual minority adult men in Toronto, Canada

2024· article· en· W4402960918 on OpenAlexafffundabout
David J. Kinitz, Lori E. Ross, Ellen MacEachen, Dionne Gesink

Bibliographic record

VenueCulture Health & Sexuality · 2024
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of WaterlooPublic Health OntarioUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaInstitute for Work and Health
KeywordsWorryTransgenderMental healthPsychologyGerontologyMedicinePsychiatryGender studiesSociologyAnxiety

Abstract

fetched live from OpenAlex

This study addresses a gap in the labour market and occupational health literatures among sexual and gender minority workers by exploring the relationship between precarious employment and mental health through a political economy framework. Narratives from 20 cisgender and transgender sexual minority men were analysed to uncover the production of employment and mental health inequities. Results are presented temporally, including employment readiness, looking for work, and on the job, illuminating the social and structural processes that underly participants' stories of precarious employment and mental health. A cyclical pattern was identified whereby participants' mental ill-health resulted in separation from the labour market and increased employment precarity that subsequently further impacted their mental health. Interventions and programmes must consider multipronged approaches that address all aspects of this syndemic, including social stigma and discrimination towards sexual and gender minority people and improved access to stable employment, mental healthcare, and adequate social welfare systems.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science 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 score0.192
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
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 head0.035
GPT teacher head0.364
Teacher spread0.329 · 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
Published2024
Admission routes3
Has abstractyes

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