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Record W2906495650 · doi:10.1177/1557988318820396

Masculinity, Mental Health, and Desire for Social Support Among Male Cancer and Infertility Patients

2018· article· en· W2906495650 on OpenAlexafffund
Skye A. Miner, Davis Daumler, Peter Chan, Abha A. Gupta, Kirk Lo, Phyllis Zelkowitz

Bibliographic record

VenueAmerican Journal of Men s Health · 2018
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsMount Sinai HospitalPrincess Margaret Cancer CentreMcGill University Health CentreMcGill UniversityUniversity of TorontoJewish General Hospital
FundersCanadian Institutes of Health Research
KeywordsMasculinityFertilityInfertilityMental healthSocial supportPsychologyMale infertilitySocial psychologyDemographyPopulationPsychiatrySociologyPregnancy

Abstract

fetched live from OpenAlex

By surveying men who are currently infertile ( N = 251) and men who are potentially infertile (i.e., men with cancer; N = 195), the mental health consequences of reproductive masculinity, or the cultural assumption that men are virile and should be fathers, were investigated. There was no difference in depression levels between these two groups when controlling for demographic variables, suggesting that both groups of men have similar mental health needs. Since gendered notions of masculinity also suggest that men do not want to discuss their fertility health, their desire for online fertility-related social support was assessed. These findings suggest that most men do want to talk to others about fertility, which indicates that there is a need for more fertility-related social support. This research challenges some conceptions regarding masculinity, as men revealed an interest in accessing online social support related to fertility.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.352
Threshold uncertainty score0.374

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.037
GPT teacher head0.380
Teacher spread0.343 · 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

Citations18
Published2018
Admission routes2
Has abstractyes

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