Effectiveness of mHealth intervention on safe abortion knowledge and perceived barriers to safe abortion services among female sex workers in Vietnam
Bibliographic record
Abstract
Background: Mobile health (mHealth) has been used to promote sexual and reproductive health (SRH) education and services; however, little is known about the use of mHealth to improve safe abortion knowledge and access to safe abortion services among female sex workers (FSWs). This study evaluated the feasibility and effectiveness of iConnect intervention through changes in knowledge on safe abortion and changes in perceived barriers to safe abortion services among FSWs in Vietnam. Methods: iConnect mobile app was developed as an interactive platform to deliver safe abortion education and referral to safe abortion services through short messaging services (SMS) enhanced by tele-counseling for 512 FSWs in Hanoi, Vietnam. A pretest-posttest evaluation was conducted using questionnaire-based phone interviews administered to 251 participants at baseline and 3 months following the intervention. Non-parametric tests evaluated the change in abortion knowledge, behaviors, and perceived barriers to safe abortion. Results: There were significant improvements in the knowledge on safe abortion among the study participants. Specifically, FSWs’ knowledge of correct gestational ages (≤22 weeks) for medical abortion increased from 78.9% at baseline to 96.8% (P=0.001). Knowledge of correct gestational ages for medical abortion at the private clinic increased from 45.3% to 63.1% (P=0.001). Knowledge on the consequences of unsafe abortion increased from 75.2% to 92.1% (P=0.001). In addition, perceived stigma and discrimination when seeking safe abortion decreased from 36.5% to 27.8% (P=0.036) and worry about the lack of confidentiality decreased from 23.3% to 15.5% (P=0.035). Conclusions: The evaluation results showed the initial effectiveness of a mobile app-based intervention in improving access to safe abortion information and services among FSWs. A future study is needed to establish the efficacy of the intervention for scaling up in Vietnam and elsewhere.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".