Patient and Provider Perspectives of a Web-Based Intervention to Support Symptom Management After Radioactive Iodine Treatment for Differentiated Thyroid Cancer: Qualitative Study
Notice bibliographique
Résumé
BACKGROUND: Patients diagnosed with differentiated thyroid cancer (DTC) who receive radioactive iodine (RAI) treatment experience acute, medium, and late treatment effects. The timing and severity of these effects varies by individual; common post-treatment effects include dry mouth, salivary gland swelling, dry eyes, and nose bleeds. The nature of symptoms that patients experience after RAI treatment can significantly and negatively impact health-related quality of life (HRQOL). Adequate information during the post-primary treatment phase remains an unmet need among the population of patients diagnosed with DTC. OBJECTIVE: This qualitative study aimed to identify and understand self-management strategies for RAI-specific symptom burden from the perspectives of patients and stakeholders (cancer care providers and patient advocates). An additional aim included assessing the features and functionalities desirable in the development of a web-based intervention to engage patients in their self-management and thyroid cancer survivorship care. METHODS: Following Social Cognitive Theory framework and person-based principles, we conducted 6 focus groups with 22 patients diagnosed with DTC who completed RAI treatment and individual interviews with 12 stakeholders in DTC care. The interviews focused on participants' perspectives on current self-management strategies and mockups of a symptom management web-based intervention. Prior to the focus groups and interviews, participants completed a demographics survey. Focus group discussions and interviews were transcribed and coded using content analysis. Inter-rater reliability was satisfactory (α=0.88). RESULTS: A total of 34 individuals (patients and stakeholders) participated in the study; mean age was 45 (SD 13.4) and 45.3 (SD 13) years, respectively. Three domains emerged from qualitative interviews: 1) Difficult-to-manage RAI symptoms: Short, medium, and late treatment effects; 2) Key intervention structure and content feedback on mockups; and 3) Intervention content to promote RAI symptom management and survivorship care. Focus group participants identified the most prevalent RAI symptoms that were difficult to manage as: dry mouth (11/22, 50%), salivary gland swelling (8/22, 36.4%), and changes in taste (12/22, 54.5%). Feedback elicited from both groups found education and symptom management mockup videos to be helpful in patient self-management of RAI symptoms, whereas patients and stakeholders provided mixed feedback on the benefits of a draft frequently asked questions page. Across focus groups and stakeholder interviews, nutrition-based symptom management strategies, communication with family members, and practical survivorship follow-up information emerged as helpful content to include on a future web-based supportive care intervention. CONCLUSIONS: Results suggest education and symptom management videos can empower patients with DTC to self-manage mild to moderate RAI symptoms on a web-based platform. Findings emphasized the need for additional information for patients related to ongoing care following RAI treatment including social support and thyroid cancer surveillance. The findings provide insights for theoretically informed interventions and recommendations for refinements in thyroid cancer survivorship from patient and provider perspectives.
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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 | 0,012 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,004 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».