‘It makes you someone who changes with the times’: health worker and client perspectives on a smartphone-based counselling application deployed in rural Tanzania
Bibliographic record
Abstract
Mobile health (mHealth) applications have been developed for community health workers (CHW) to help simplify tasks, enhance service delivery and promote healthy behaviours. These strategies hold promise, particularly for support of pregnancy and childbirth in low-income countries (LIC), but their design and implementation must incorporate CHW clients' perspectives to be effective and sustainable. Few studies examine how mHealth influences client and supervisor perceptions of CHW performance and quality of care in LIC. This study was embedded within a larger cluster-randomized, community intervention trial in Singida, Tanzania. CHW in intervention areas were trained to use a smartphone application designed to improve data management, patient tracking and delivery of health messages during prenatal counselling visits with women clients. Qualitative data collected through focus groups and in-depth interviews illustrated mostly positive perceptions of smartphone-assisted counselling among clients and supervisors including: increased quality of care; and improved communication, efficiency and data management. Clients also associated smartphone-assisted counselling with overall health system improvements even though the functions of the smartphones were not well understood. Smartphones were thought to signify modern, up-to-date biomedical information deemed highly desirable during pregnancy and childbirth in this context. In this rural Tanzanian setting, mHealth tools positively influenced community perceptions of health system services and client expectations of health workers; policymakers and implementers must ensure these expectations are met. Such interventions must be deeply embedded into health systems to have long-term impacts on maternal and newborn health outcomes.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".