Development of a Self-management and Peer-Mentoring Intervention to Improve Transition Readiness Among Young Adult Survivors of Pediatric Cancer: Formative Qualitative Research Study
Notice bibliographique
Résumé
BACKGROUND: Childhood cancer survivors require lifelong risk-based follow-up care. It should be noted that less than one-third of adult survivors of childhood cancer report any survivor-focused care, and fewer than 1 in 5 obtain risk-based follow-up care. It is thought that this may be due to inadequate transition readiness, including low levels of knowledge, skills, motivation, and resources to make the transition to independent self-management of follow-up care. Interventions that focus specifically on improving the transition from parent-managed to self-managed care are needed. Theory and prior research suggest that targeting self-management skills and using peer mentoring may be innovative strategies to improve transition readiness. OBJECTIVE: This study aims to identify the content of a self-management intervention to improve transition readiness among adolescent and young adult (AYA) survivors. METHODS: Intervention development occurred in 3 stages: formative research with AYA survivors to identify barriers and facilitators to obtaining risk-based survivorship care, content development using feedback from multiple stakeholders (AYA survivors, parents, and providers), and content refinement (usability testing) of the initial proposed educational modules for the program. Content analysis, guided by the social-ecological model of AYA readiness for transition, was used to identify themes and develop and refine the content for the intervention. RESULTS: A total of 19 AYA survivors participated in the formative research stage, and 10 AYA survivors, parents, and health care providers participated in the content development and refinement stages. The major barrier and facilitator themes identified included knowledge of cancer history and risks; relationships with health care providers; relationships with family members involved in care; emotions about health, follow-up care, and transfer of care; and lifestyle behaviors and life transitions. These themes were translated into 5 self-management modules: understanding treatment history and the survivorship care plan, managing health care logistics and insurance, communicating with health care providers and family members involved in care, dealing with emotions, and staying healthy in the context of life transitions. Feedback from the key stakeholders indicated that the content was relevant but should include participative elements (videos and tailored feedback) to make the intervention more engaging. The AYA survivors were receptive to the idea of working with a peer mentor and expressed a preference for using SMS text messaging, telephone calls, or videoconference to communicate with their mentor. CONCLUSIONS: Incorporating AYA survivors, parents, and providers in the design was essential to developing the content of a self-management and peer-mentoring intervention. AYA survivors confirmed the important targets for the intervention and facilitated design decisions in line with our target users' preferences. The next step will be to conduct a single-arm trial to determine the feasibility and acceptability of the proposed intervention among AYA survivors of childhood cancer.
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 | 0,019 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
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 ».