Provider Perspectives on Sexual Health Services Used by Bangladeshi Women with mHealth Digital Approach: A Qualitative Study
Bibliographic record
Abstract
Cases of sexually transmitted infections (STIs) are underreported in Bangladesh. Women in general suffer from poor sexual health outcomes due to a lack of access to sexual health services. mHealth, a digital approach to STI services, is an easier and cheaper way to disseminate health information in Bangladesh. However, women have less autonomy in accessing STI services and it is important to learn if, how and/or why women use mHealth. A qualitative study was conducted with 26 medical doctors to explore their perceptions of the mHealth STI services used by Bangladeshi women. Themes were grouped under four categories: (1) provider perceptions of mHealth for sexual healthcare; (2) the health literacy of women clients; (3) cost and maintaining timeliness in providing mHealth services; (4) mHealth service accessibility. Data suggest that mHealth can play a significant role in improving the awareness and utilization of STI services in Bangladeshi women. Successful opportunities for STI service expansion using mHealth were identified, depending on the quality and type of service delivery options, awareness of challenges related to health literacy framework, cost, accessibility to information and availability of culturally competent health experts to disseminate health information. We identify the need to increase access and use of mHealth services for sexual health, as it provides an innovative platform to bridge the health communication gaps in sexual health for Bangladeshi women.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".