mHealth information for migrants: an e-health intervention for internal migrants in Vietnam
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Socio-economic development in Vietnam has resulted in increased internal migration particularly among young women seeking employment opportunities in cities. Vietnamese female migrants who enter new environments often encounter the loss or neglect of their right to access sexual and reproductive health services. To address this, a mobile health (mHealth) intervention model was implemented over 12 months (2013-2014) in a factory in the Long Bien industrial zone of Hanoi, Vietnam. METHODS: The intervention provided sexual and reproductive health services for female migrants through text messaging, information booklets accompanied maps, and free counseling via a hotline. To evaluate the impact of the intervention, pre- and post-intervention data were collected to measure changes in women's knowledge and practices related to sexual and reproductive health. Qualitative data in the form of personal interviews were also collected. The sample size for the baseline survey was 411 women, and for the post-intervention survey it was 482 women (the intervention involved an open cohort). The majority of women were unmarried and under the age of 25. RESULTS: Results indicate that there was high uptake of the intervention services and that most women found the services important and useful. In addition, there was evidence that the intervention (1) increased women's knowledge of sexual and reproductive health (e.g., proper use of condoms, identification of high-risk behaviors such as having unprotected sex), and (2) fostered improved practices related to sexual and reproductive health (e.g., increased gynecological check-ups and use of condoms). CONCLUSIONS: The study demonstrated the feasibility of implementing a multi-faceted intervention for migrant women working in an industrial zone in Hanoi, Vietnam as well as its successful uptake and some early positive effects. This can be used to inform future design and implementation of mHealth/eHealth intervention models for migrant and other vulnerable/hard to reach population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it