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Relational Body Image: Developing an Understanding of How and Why Body Image Changes Across Specific Relationships

2023· dissertation· en· W7033607072 sur OpenAlexfundno aff

Notice bibliographique

RevueUWSpace (University of Waterloo) · 2023
Typedissertation
Langueen
DomainePsychology
ThématiqueEating Disorders and Behaviors
Établissements canadiensnon disponible
Organismes subventionnairesUniversity of Waterloo
Mots-clésConceptualizationIntrapersonal communicationImage (mathematics)TraitSelf-conceptEmpirical researchHuman physical appearanceInterpersonal relationship
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Studies show that social relationships strongly influence body image and that an individual’s body image changes with social-contextual factors. Nevertheless, research has conceptualized and measured body image as an internal, intrapersonal concept even though, in social psychological research, there is a strong tradition of viewing the self and self-related attitudes as interpersonally-based. In my program of research, I suggest that these social psychological perspectives may be relevant to our understanding of body image. Drawing on this work, I developed a novel, relational conceptualization of body image. I coined the term relational body image to indicate that an individual may experience changes in their body image across their close relationships based on the perceived characteristics of the other person. My primary research objective was to obtain empirical support for the existence of relational body image. A secondary objective was to expand and deepen my conceptualization of relational body image using a diverse range of theories and methods relevant to the study of relationships. My program of research consisted of three studies with cis-gendered college women. In Study 1, I devised a way to measure relational body image by incorporating egocentric network methods and adapting existing trait body image-related measures into relationship-specific measures. Using these methods, 87 women generated a list of people in their social networks and 10 close others were randomly selected. Participants then provided ratings of their body image and eating behaviours with each of their 10 others and reported on the body image-related characteristics of those people. Multilevel modelling provided initial support for relational body image, demonstrating that a given woman’s body image fluctuated across her relationships and was predicted by the other person’s perceived characteristics. In Study 2, I explored individual differences in relational body image and tested the hypothesis that women who are more body-dissatisfied would experience more extreme changes in their overall body image levels across their relationships. In a sample of 189 women, results from multiverse analysis supported this hypothesis. Findings thus pointed to the particular relevance of relational body image for body-dissatisfied women. In Study 3, I aimed to further enhance my conceptualization of relational body image by exploring the subjective experiences of body-dissatisfied women. In a one-to-one interview, 18 women rated their body image with seven close others. A graph depicting each woman’s personal body image with their seven others was created and used to prompt discussion about their subjective experiences of relational body image. Reflexive thematic analysis identified one overarching theme, which suggested that relational body image is made up of a complex configuration of different factors within a specific relationship. Three subthemes indicated that these different factors: give rise to a general expectation for how the other person will view one’s body; are constantly in-flux; and differ in how much power they exert on the individual. Study 3 thus added novel methodological support and further theoretical nuance to my conceptualization of relational body image. Overall, my program of research was successful in integrating a wide range of theories and methods to provide empirical support for relational body image. Potential applications are widespread. Indeed, results suggest there may be merit in assessing, preventing, and treating body-image related concerns with a focus on specific relationships. In addition, my program of research provides a framework for how to derive and test novel concepts within body image research. In particular, the current program highlights the downstream empirical benefits of conducting research that is strongly and broadly theory-driven and investigated through diverse methodologies. Research on relational body image thus has applied, theoretical, and methodological implications.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,006
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,014

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,006
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0010,005
Communication savante0,0040,008
Science ouverte0,0010,002
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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.

Tête enseignante Opus0,082
Tête enseignante GPT0,293
Écart entre enseignants0,211 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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