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Record W4393951481 · doi:10.32920/25437142

Psychometric Properties of the Social Appearance Anxiety Scale Among Canadian Gay and Bisexual Men of Color

2024· preprint· en· W4393951481 on OpenAlexaboutno aff
Trevor Hart, Nooshin Khobzi Rotondi, Rusty Souleymanov, David J. Brennan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicBody Image and Dysmorphia Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyScale (ratio)Social anxietyAnxietyClinical psychologyDevelopmental psychologySocial psychologyPsychiatryGeographyCartography

Abstract

fetched live from OpenAlex

<p>Gay and bisexual men (GBM) are an underexamined population in body image research, given the relatively high levels of body dissatisfaction reported among GBM compared with that of heterosexual men. However, distress related to appearance among GBM may not be exclusive to anxiety about having a perfect physique. The present study used the 16-item Social Appearance Anxiety Scale (SAAS; Hart, Flora, et al., 2008) in a racially diverse sample of 389 GBM of color to examine the psychometric properties of a measure of anxiety about being evaluated for one’s overall appearance. Similar to other studies of undergraduate students (e.g., Levinson & Rodebaugh, 2011; Levinson et al., 2013), the SAAS had a unifactorial structure and was highly internally consistent. Social appearance anxiety was highly correlated with anxiety and body image dissatisfaction. Social appearance anxiety had moderate to large correlations with other established measures of body image dissatisfaction and psychological distress. Racist experiences were associated with social appearance anxiety above and beyond correlations of social appearance anxiety with anxiety and body image dissatisfaction. The present study also extends beyond previous research on body image and anxiety among GBM by demonstrating the importance of minority stress-related variables that go beyond sexual orientation, such as racism experiences. (PsycINFO Database Record (c) 2019 APA, all rights reserved)</p>

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 score0.294
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.049
GPT teacher head0.311
Teacher spread0.262 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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