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Record W2119142496 · doi:10.1177/1049732310365700

Connecting Masculinity and Depression Among International Male University Students

2010· article· en· W2119142496 on OpenAlexafffundabout
John L. Oliffe, Steve Robertson, Mary T. Kelly, Philippe Roy, John S. Ogrodniczuk

Bibliographic record

VenueQualitative Health Research · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsUniversité LavalUniversity of British Columbia
FundersCanadian Institutes of Health ResearchHealth CanadaInstitute of Gender and HealthMichael Smith Health Research BC
KeywordsMasculinityDepression (economics)PsychologyPerceptionQualitative researchClinical psychologyIsolation (microbiology)Sociology

Abstract

fetched live from OpenAlex

International university students can experience isolation amid academic pressures. Such circumstances can manifest as or exacerbate depression. This qualitative study involved 15 international male students at a Canadian university who were diagnosed or self-identified as having depression. Individual interviews revealed men's perspectives about causes, implications, and management of depression. Participants intertwined sex- and gender-based factors in detailing causes, and emphasized the potential for parents to impact depression. Implications of depression for embodying traditional masculine roles of breadwinner and career man influenced many men to filter details about their illness within "home" cultures. This practice often prevailed within Canada despite the men's perceptions that greater societal acceptance existed. Masculine ideals underpinned self-management strategies to fight depression and regain control. Counter to men's reluctance to disclose illness details were participants' self-management preference for peer-based support. Study findings highlight how masculine ideals and cultural constructs can influence depression experiences and expressions.

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)0.011
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.221
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
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 head0.324
GPT teacher head0.589
Teacher spread0.264 · 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 designQualitative
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

Citations51
Published2010
Admission routes3
Has abstractyes

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