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Record W2618739084 · doi:10.1016/j.jsxm.2017.04.356

Anal Pain and Penetration Cognitions in Men Who Have Sex with Men: A Pilot Study

2017· article· en· W2618739084 on OpenAlexaff
Stéphanie E. M. Gauvin, Caroline F. Pukall

Bibliographic record

VenueThe Journal of Sexual Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicSexual function and dysfunction studies
Canadian institutionsKingston Health Sciences CentreQueen's University
Fundersnot available
KeywordsCognitionDistressPsychologyClinical psychologyMen who have sex with menAnal sexPain catastrophizingMedicineChronic painPsychiatryHuman immunodeficiency virus (HIV)

Abstract

fetched live from OpenAlex

Anodyspareunia is described as recurrent and persistent anal pain during receptive anal sex (Rosser et al., 1998), and is a frequently reported sexual problem amongst men who have sex with men (MSM) (Peixoto & Nobre, 2014). Research on female sexual pain suggests that negative penetration cognitions are associated with greater impairment in female sexual and pain outcomes, and positive cognitions about penetration are associated with improved sexual and pain outcomes (Anderson et al., 2016). The role of penetration cognitions in anal pain, however, has yet to be investigated. The current study examined the associations between anal penetration cognitions and sexual well-being outcomes in MSM self-reporting anal pain during receptive penetration. A sample of N = 70 MSM completed online measures assessing anal penetration cognitions, including subscales assessing (a) control cognitions, (b) catastrophic and pain cognitions, (c) negative self-image cognitions, and (d) positive cognitions. Participants also completed measures of the frequency, severity, and distress of their anal pain, sexual satisfaction, and sexual functioning.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.076
GPT teacher head0.339
Teacher spread0.263 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2017
Admission routes1
Has abstractyes

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