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Record W4210510522 · doi:10.1080/00224499.2022.2029810

Men’s Feminist Identification and Reported Use of Prescription Erectile Dysfunction Medication

2022· article· en· W4210510522 on OpenAlexaffabout
Tony Silva, Tina Fetner

Bibliographic record

VenueThe Journal of Sex Research · 2022
Typearticle
Languageen
FieldMedicine
TopicSexual function and dysfunction studies
Canadian institutionsMcMaster UniversityUniversity of British Columbia
Fundersnot available
KeywordsErectile dysfunctionMedical prescriptionIdentification (biology)MedicinePsychologyGynecologyPsychiatryClinical psychologyPharmacology

Abstract

fetched live from OpenAlex

We analyzed data from the 2018 Sex in Canada survey (n = 1,015 cisgender men) to examine the association between feminist identification and reported use of prescription ED medication (EDM) during men’s last sexual encounter. Feminist-identified men were substantially more likely to report EDM use than non-feminist men, even after controlling for alcohol use before sex, erection difficulties, sexual arousal, sexual health, mental health, and physical health. One explanation is that feminist men may use EDM to bolster their masculinity when it is otherwise threatened by their identification as feminist. Another is that non-feminist men may be less likely to use prescription EDM because they view accessing healthcare services as a threat to their masculinity. It is also possible that feminist men are more likely to use EDM because they wish to maintain an erection to better please their partner. Lastly, feminist men may be more honest about EDM use than non-feminist men, even though rates are similar. Regardless of the exact reason, therapists can use these results to tailor sexual health messages to clients based on feminist identification. Future work could employ qualitative methods to understand why feminist men report higher rates of EDM use than non-feminist men.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.496
Threshold uncertainty score0.376

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
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 integrity0.0000.001
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 head0.210
GPT teacher head0.406
Teacher spread0.196 · 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 designNot applicable
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

Citations3
Published2022
Admission routes2
Has abstractyes

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