Development of a Culturally Tailored Text Message Maternal Health Program: TextMATCH
Bibliographic record
Abstract
BACKGROUND: Mobile phones are increasingly being used to deliver health information and health services globally. Mobile health (mHealth) interventions may be well-suited for minority groups with greater barriers to accessing traditional health services. However, little has been written about the process of culturally adapting interventions for multiple ethnic and cultural minorities within a population. OBJECTIVE: This study describes the process of developing a culturally tailored text message-based maternal health program (TextMATCH: Text for MATernal and Child Health) for Māori, Pacific, Asian, and South Asian families living in New Zealand. We report on engagement and acceptability of the TextMATCH program. METHODS: Program data was examined to describe engagement with the program 18 months after implementation. Telephone interviews were conducted with a sample of participants who consented to provide feedback on acceptability and relevance of the program. RESULTS: A total of 1404 participants enrolled in TextMATCH over 18 months, with 18.52% (260) actively opting out at some point (after 0 to 17 months of messages). It was found that 356 (70.9%) of the 502 eligible participants actively switched from the initial pregnancy program to the baby program after delivery. Phone interviews were conducted with 29 participants including 6 who had withdrawn (duration of program from 3 to 16 months). Only 2 participants reported that the program was not useful, with the remainder rating the usefulness of messages positively (average 4.24 out of 5). All participants stated that the messages were relevant, culturally appropriate, and easy to understand. Most were happy with the specific advice and the language options provided. CONCLUSIONS: We have demonstrated the importance of an intensive approach to the development of a culturally adapted and tailored mHealth program for multiple different cultural minority groups within our population.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".