A Proposed mHealth Intervention to Address Patient Barriers to Colposcopy Attendance: Qualitative Interview Study of Clinic Staff and Patient Perspectives
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".