Patterns of Mobile Phone Ownership and Use Among Pregnant Women in Southern Tanzania: Cross-Sectional Survey
Bibliographic record
Abstract
BACKGROUND: There is a paucity of subnational data on patterns of mobile phone ownership and use in Tanzania to inform the development of digital health interventions. OBJECTIVE: The aim of this study is to assess patterns of mobile phone ownership and use in pregnant women to inform the feasibility and design of digital health interventions for promoting timely uptake of childhood vaccines in southern Tanzania. METHODS: Between August and November 2017, pregnant women in their third trimester were enrolled at health facilities and from surrounding communities, and asked about their patterns of mobile phone ownership and use in an interviewer administered survey. RESULTS: Of 406 women, only 3 had never used a phone. Most women (>98%) could make and receive phone calls. Compared to urban women, rural women reported higher mobile phone use rates but were less likely to be sole owners of phones, and less likely to send or receive SMS, transact money, browse the internet, or use social media via mobile phones. CONCLUSIONS: The findings suggest high feasibility for digital health interventions delivered via mobile phones to pregnant women in southern Tanzania. The feasibility of smartphone-based interventions or strategies relying on the use of social media or the internet is limited.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".