Acceptability and implementation challenges of smartphone-based training of community health nurses for visual inspection with acetic acid in Ghana: mHealth and cervical cancer screening
Bibliographic record
Abstract
OBJECTIVE: To explore acceptability and feasibility of smartphone-based training of low-level to mid-level health professionals in cervical cancer screening using visual inspection with acetic acid (VIA)/cervicography. DESIGN: In 2015, we applied a qualitative descriptive approach and conducted semi-structured interviews and focus groups to assess the perceptions and experiences of community health nurses (CHNs) (n=15) who performed smartphone-based VIA, patients undergoing VIA/cryotherapy (n=21) and nurse supervisor and the expert reviewer (n=2). SETTING: Community health centres (CHCs) in Accra, Ghana. RESULTS: The 3-month smartphone-based training and mentorship was perceived as an important and essential complementary process to further develop diagnostic and management competencies. Cervical imaging provided peer-to-peer learning opportunities, and helped better communicate the procedure to and gain trust of patients, provide targeted education, improve adherence and implement quality control. None of the patients had prior screening; they overwhelmingly accepted smartphone-based VIA, expressing no significant privacy issues. Neither group cited significant barriers to performing or receiving VIA at CHCs, the incorporation of smartphone imaging and mentorship via text messaging. CHNs were able to leverage their existing community relationships to address a lack of knowledge and misperceptions. Patients largely expressed decision-making autonomy regarding screening. Negative views and stigma were present but not significantly limiting, and the majority felt that screening strategies were acceptable and effective. CONCLUSIONS: Our findings suggest the overall acceptability of this approach from the perspectives of all stakeholders with important promises for smartphone-based VIA implementation. Larger-scale health services research could further provide important lessons for addressing this burden in low-income and middle-income countries.
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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 | 0.002 | 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 | 0.000 |
| 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".