An mHealth Model to Increase Clinic Attendance for Breast Symptoms in Rural Bangladesh: Can Bridging the Digital Divide Help Close the Cancer Divide?
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: To demonstrate proof of concept for a smart phone-empowered community health worker (CHW) model of care for breast health promotion, clinical breast examination (CBE), and patient navigation in rural Bangladesh. METHODS: This study was a randomized controlled trial; July 1 to October 31, 2012, 30 CHWs conducted door-to-door interviews of women aged 25 and older in Khulna Division. Only women who disclosed a breast symptom were offered CBE. Arm A: smart phone with applications to guide interview, report data, show motivational video, and offer appointment for women with an abnormal CBE. Arm B: smart phone/applications identical to Arm A plus CHW had training in "patient navigation" to address potential barriers to seeking care. Arm C: control arm (no smart phone; same interview recorded on paper). Outcomes are presented as the "adherence" (to advice regarding a clinic appointment) for women with an abnormal CBE. This study was approved by Women's College Hospital Research Ethics Board (Toronto, Ontario, Canada) and district government officials (Khulna, Bangladesh). Funded by Grand Challenges Canada. RESULTS: In 4 months, 22,337 women were interviewed; <1% declined participation, and 556 women had an abnormal CBE. Control group CHWs completed fewer interviews, had inferior data quality, and identified significantly fewer women with abnormal breast exams compared with CHWs in arms A and B. Arm B had the highest adherence. CONCLUSION: CHWs guided by our smart phone applications were more efficient and effective in breast health promotion compared with the control group. CHW "navigators" were most effective in encouraging women with an abnormal breast examination to adhere to advice regarding clinic attendance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it