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Record W2599556138

Effect of gender socialization on the presentation of depression among men

2011· article· en· W2599556138 on OpenAlexvenueaboutno aff
Jennifer K. Wide, Hiram Mok, Mario McKenna, John S. Ogrodniczuk

Bibliographic record

VenueCanadian Family Physician · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMasculinityConformityDepression (economics)DistressSocializationClinical psychologyEmotional distressMedicinePsychologyPsychiatryMale genderAnxietyDevelopmental psychologySocial psychologyInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Objective To examine the association between men’s conformity to masculine norms and depression. Design Cross-sectional analysis. Setting University family practice clinic in Vancouver, BC. Participants Male patients, 19 years of age and older (N = 97). Main outcome measures The relationships among patients’ scores on the Brief Symptom Inventory–18 depression subscale, Gotland Male Depression Scale, and Conformity to Masculine Norms Inventory, and whether or not patients were prompted to discuss emotional concerns with their physicians after completing these screening tests. Results Conformity to masculine norms was significantly associated with depression as assessed by the male depression screen ( P = .039), but not with the screen that assessed typical depressive symptoms ( P = .068). Men, regardless of their degree of masculinity or distress, overwhelmingly did not disclose emotional concerns to their physicians, even if the content of their distress involved suicidal thoughts. Conclusion Male depression screens might capture aspects of depression associated with masculine gender socialization that are not captured by typical measures of depression. Given the tendency of men to not disclose emotional distress to their family physicians, potentially high-risk cases could be missed without direct inquiry by clinicians.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.574
Threshold uncertainty score0.943

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.042
GPT teacher head0.279
Teacher spread0.237 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2011
Admission routes2
Has abstractyes

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