A Proposed mHealth Intervention to Address Patient Barriers to Colposcopy Attendance: Qualitative Interview Study of Clinic Staff and Patient Perspectives
Notice bibliographique
Résumé
BACKGROUND: Cervical cancer disparities persist among minoritized women due to infrequent screening and poor follow-up. Structural and psychosocial barriers to following up with colposcopy are problematic for minoritized women. Evidence-based interventions using patient navigation and tailored telephone counseling, including the Tailored Communication for Cervical Cancer Risk (TC3), have modestly improved colposcopy attendance. However, the efficacious TC3 intervention is human resource-intense and could have greater reach if adapted for mobile health, which increases convenience and access to health information. OBJECTIVE: This study aimed to describe feedback from clinic staff members involved in colposcopy processes and patients referred for colposcopy regarding adaptions to the TC3 phone-based intervention to text messaging, which addresses barriers among those referred for colposcopy after abnormal screening results. METHODS: Semistructured depth qualitative interviews were conducted over Zoom [Zoom Communications, Inc] or telephone with a purposive sample of 22 clinic staff members (including clinicians and support staff members) and 34 patients referred for colposcopy from 3 academic obstetrics and gynecology (OB-GYN) clinics that serve predominantly low-income, minoritized patients in different urban locations in New Jersey and Pennsylvania. Participants were asked about colposcopy attendance barriers and perspectives on a proposed text message intervention to provide tailored education and support in the time between abnormal cervical screening and colposcopy. The analytic team discussed interviews, wrote summaries, and consensus-coded transcripts, analyzing output for emergent findings and crystallizing themes. RESULTS: Clinic staff members and patients had mixed feelings about a text-only intervention. They overwhelmingly perceived a need to provide patients with appointment reminders and information about abnormal cervical screening results and colposcopy purpose and procedure. Both groups also thought messages emphasizing that human papillomavirus is common and cervical cancer can be prevented with follow-up could enhance attendance. However, some had concerns about the privacy of text messages and text fatigue. Both groups thought that talking to clinic staff members was needed in certain instances; they proposed connecting patients experiencing complex psychosocial or structural barriers to staff members for additional information, psychological support, and help with scheduling around work and finding childcare and transportation solutions. They also identified inadequate scheduling and reminder systems as barriers. From this feedback, we revised our text message content and intervention design, adding a health coaching component to support patients with complex barriers and concerns. CONCLUSIONS: Clinic staff members and patient perspectives are critical for designing appropriate and relevant interventions. These groups conveyed that text message-only interventions may be useful for patients with lesser barriers who may benefit from reminders, basic educational information, and scheduling support. However, multimodal interventions may be necessary for patients with complex barriers to colposcopy attendance, which we intend to evaluate in a subsequent trial.
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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 | 0,015 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,001 | 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 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».