A Theoretically Grounded Exploration of Individual and Family Self-Management of Polycystic Ovary Syndrome in Adolescents.


Journal

Comprehensive child and adolescent nursing
ISSN: 2469-4207
Titre abrégé: Compr Child Adolesc Nurs
Pays: England
ID NLM: 101682864

Informations de publication

Date de publication:
Dec 2020
Historique:
pubmed: 24 10 2019
medline: 15 7 2021
entrez: 24 10 2019
Statut: ppublish

Résumé

Polycystic ovary syndrome (PCOS) is highly prevalent and increasingly diagnosed during adolescence. This study explored the context and processes of self-management among adolescents, and parents of adolescents, who have PCOS. Adolescents with PCOS (n = 7) and their parents (n = 8) participated in a series of focus groups. Deductive thematic analysis was guided by the Individual and Family Self-Management Theory (IFSMT), and the patterns that were identified aligned with the contexts and processes described therein. A secondary inductive approach was employed as a novel contextual pattern emerged: psychological health and well-being. Study findings suggest that adolescents and their families have a desire to engage in optimal self-management, and particularly effective strategies may consider a holistic, family intervention approach that addresses psychological health and well-being in addition to behavior change. Study findings are the first to reflect the unique needs of adolescents with PCOS and their families.

Identifiants

pubmed: 31644327
doi: 10.1080/24694193.2019.1679278
pmc: PMC7176521
mid: NIHMS1541416
doi:

Types de publication

Journal Article

Langues

eng

Pagination

348-362

Subventions

Organisme : NINR NIH HHS
ID : P30 NR015335
Pays : United States

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Auteurs

Cara C Young (CC)

School of Nursing, University of Texas System , Austin, Texas, USA.

Atami O Sagna (AO)

School of Nursing, University of Texas System , Austin, Texas, USA.

Maria Monge (M)

Dell Medical School, University of Texas System , Austin, Texas, USA.

Lynn Rew (L)

School of Nursing, University of Texas System , Austin, Texas, USA.

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Classifications MeSH