Mobile Health Initiatives in Vietnam: Scoping Study
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth) offers a promising solution to the multitude of challenges the Vietnamese health system faces, but there is a scarcity of published information on mHealth in Vietnam. OBJECTIVE: The objectives of this scoping study were (1) to summarize the extent, range, and nature of mHealth initiatives in Vietnam and (2) to examine the opportunities and threats of mHealth utilization in the Vietnamese context. METHODS: This scoping study systematically identified and extracted relevant information from 20 past and current mHealth initiatives in Vietnam. The study includes multimodal information sources, including published literature, gray literature (ie, government reports and unpublished literature), conference presentations, Web-based documents, and key informant interviews. RESULTS: We extracted information from 27 records from the electronic search and conducted 14 key informant interviews, allowing us to identify 20 mHealth initiatives in Vietnam. Most of the initiatives were primarily funded by external donors (n=15), while other initiatives were government funded (n=1) or self-funded (n=4). A majority of the initiatives targeted vulnerable and hard-to-reach populations (n=11), aimed to prevent the occurrence of disease (n=12), and used text messaging (short message service, SMS) as part of their intervention (n=14). The study revealed that Vietnamese mHealth implementation has been challenged by factors including features unique to the Vietnamese language (n=4) and sociocultural factors (n=3). CONCLUSIONS: The largest threats to the popularity of mHealth initiatives are the absence of government policy, lack of government interest, heavy dependence on foreign funding, and lack of technological infrastructure. Finally, while current mHealth initiatives have already demonstrated promising opportunities for alternative models of funding, such as social entrepreneurship or private business models, sustainable mHealth initiatives outside of those funded by external donors have not yet been undertaken.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.020 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".