mHealth to Train Community Health Nurses in Visual Inspection With Acetic Acid for Cervical Cancer Screening in Ghana
Bibliographic record
Abstract
OBJECTIVE: There is a shortage of trained health care personnel for cervical cancer screening in low-/middle-income countries. We evaluated the feasibility and limited efficacy of a smartphone-based training of community health nurses in visual inspection of the cervix under acetic acid (VIA). MATERIALS AND METHODS: During April to July 2015 in urban Ghana, we designed and developed a study to determine the feasibility and efficacy of an mHealth-supported training of community health nurses (CHNs, n = 15) to perform VIA and to use smartphone images to obtain expert feedback on their diagnoses within 24 hours and to improve VIA skills retention. The CHNs completed a 2-week on-site introductory training in VIA performance and interpretation, followed by an ongoing 3-month text messaging-supported VIA training by an expert VIA reviewer. RESULTS: Community health nurses screened 169 women at their respective community health centers while receiving real-time feedback from the reviewer. The total agreement rate between all VIA diagnoses made by all CHNs and the expert reviewer was 95%. The mean (SD) rate of agreement between each CHN and the expert reviewer was 89.6% (12.8%). The agreement rates for positive and negative cases were 61.5% and 98.0%, respectively. Cohen κ statistic was 0.67 (95% CI = 0.45-0.88). Around 7.7% of women tested VIA positive and received cryotherapy or further services. CONCLUSIONS: Our findings demonstrate the feasibility and efficacy of mHealth-supported VIA training of CHNs and have the potential to improve cervical cancer screening coverage in Ghana.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".