Multistakeholder Perspectives on Maternal Text Messaging Intervention in Uganda: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Despite continued interest in the use of mobile health for improving maternal health outcomes, there have been limited attempts to identify relevant program theories. OBJECTIVES: This study had two aims: first, to explicate the assumptions of program designers, which we call the program theory and second, to contrast this program theory with empirical data to gain a better understanding of mechanisms, facilitators, and barriers related to the program outcomes. METHODS: To achieve the aforementioned objectives, we conducted a retrospective qualitative study of a text messaging (short message service) platform geared at improving individual maternal health outcomes in Uganda. Through interviews with program designers (n=3), we elicited 3 main designers' assumptions and explored these against data from qualitative interviews with primary beneficiaries (n=26; 15 women and 11 men) and health service providers (n=6), as well as 6 focus group discussions with village health team members (n=50) who were all involved in the program. RESULTS: Our study results highlighted that while the program designers' assumptions were appropriate, additional mechanisms and contextual factors, such as the importance of incentives for village health team members, mobile phone ownership, and health system factors should have been considered. CONCLUSIONS: Our results indicate that text messages could be an effective part of a more comprehensive maternal health program when context and system barriers are identified and addressed in the program theories.
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.
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 | 0.020 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".