(058) Attention to Genital Cues Scale - Principal Components Analysis and Correlational Validity
Notice bibliographique
Résumé
Abstract Introduction The Attention to Genital Cues (AGC) scale was developed to characterize respondents’ usual experience observing genital sensations by capturing perceived prevalence, ease, and value of being aware of genital cues (Handy & Meston, 2016, 2018). The scale has been predominantly used in the literature as a descriptive measure (A. Handy, personal communication, October 13, 2020). However, there appears to be no published data for using it in quantitative statistical analyses. Quantitative information about genital cue observation may be useful, as it has been reported that awareness of these sensations is salient for some women’s sexual arousal (Meston & Stanton, 2018). Objectives The objective of this investigation was to explore the psychometric properties of the AGC scale. Methods This was a secondary data analysis of 2018 data collected from student and community participants regarding genital arousal sensations. The current investigation focused on responses to the AGC scale, which consists of an initial skip pattern question followed by four scored items and a free-text response (Handy & Meston, n.d.). The sample was limited to women who endorsed usually paying attention to genital cues when sexually engaged and/or aroused (N = 491). A principal components analysis was performed on the four scored items to determine whether the items comprised a single factor; bivariate correlations were calculated to determine convergent and discriminant validity as well as to explore other potential associations of interest. Variables used for the latter analysis were subscales of the Multidimensional Assessment of Interoceptive Awareness scale (Mehling et al., 2012), which measures bodily mindfulness (noticing, not-distracting, not-worrying, attention regulation, emotional awareness, self-regulation, body listening, and trusting); the arousal subscale of the Female Sexual Function Index (Rosen et al., 2000); and the tiredness and negative arousal subscales of the Four Dimensional Mood Scale (Huelsman et al., 1998). Results A principal components analysis produced a .62 Kaiser-Meyer-Olkin measure of sampling adequacy, which surpassed the value of .50 suggested as adequate by Kaiser (1970), and a significant Bartlett’s test of sphericity (χ2 (6) = 311.15, p < .00) (Barlett, 1950). All of the item loadings were above .40 (.56 - .82) on a single factor for genital attending; therefore, rotation was not necessary. Reliability for the measure was adequate (α = .64), especially given the relatively small number of items. There was some support for the measure’s convergent validity: the AGC was positively associated with attention regulation (r = .12, p < .01), noticing (r =.19, p < .00), and sexual arousal (r = .198, p < .009). However, a significant association was not found between AGC and body listening (r = .03, p < .47). AGC scores were not significantly related to dispositional tiredness (r = .03, p < .54) or negative affect (r = -.12, p < .01), demonstrating discriminant validity. Additionally, age (r = .18, p < .00), self-regulation (r = .11, p < .02), and trusting (r = .10, p < .02) were significantly correlated with AGC scores. Conclusions The AGC scale has adequate psychometric properties to function as a single factor scale. This measure may be useful in future quantitative investigations focused on sexual psychophysiology, sexual functioning, or bodily mindfulness. Disclosure No.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».