Implementation of the PIERS on the Move mHealth Application From the Perspective of Community Health Workers and Nurses in Rural Mozambique
Bibliographic record
Abstract
Background: mHealth is increasingly regarded as having the potential to support service delivery by health workers in low-resource settings. PIERS on the Move (POM) is a mobile health application developed to support community health workers identification and management of women at risk of adverse outcomes from pre-eclampsia. The objective of this study was to evaluate the impact of using POM in Mozambique on community health care workers' knowledge and self-efficacy related to caring for women with pre-eclampsia, and their perception of usefulness of the tool to inform implementation. Method: An evaluation was conducted for health care workers in the Mozambique Community Level Intervention for Pre-eclampsia (CLIP) cluster randomized trial from 2014 to 2016 in Maputo and Gaza provinces (NCT01911494). A structured survey was designed using themes from the Technology Acceptance Model, which describes the likelihood of adopting the technology based on perceived usefulness and perceived ease of use. Surveys were conducted in Portuguese and translated verbatim to English for analysis. Preliminary analysis of open-ended responses was conducted to develop a coding framework for full qualitative analysis, which was completed using NVivo12 (QSR International, Melbourne, Australia). Results: Overall, 118 community health workers (44 intervention; 74 control) and 55 nurses (23 intervention; 32 control) were surveyed regarding their experiences. Many community health workers found the POM app easy to use (80%; 35/44), useful in guiding their activities (68%; 30/44) and pregnant women received their counseling more seriously because of the POM app (75%; 33/44). Almost a third CHWs reported some challenges using the POM app (30%; 13/44), including battery depletion after a full day's activity. Community health workers reported increases in knowledge about pre-eclampsia and other pregnancy complications and increases in confidence, comfort and capacity to advise women on health conditions and deliver services. Nurses recognized the increased capacity of community health workers and were more confident in their clinical and technological skills to identify women at risk of obstetric complications. Conclusions: Many of the community health workers reported that POM improved knowledge, self-efficacy and strengthened relationships with the communities they serve and local nurses. This helped to strengthen the link between community and health facility. However, findings highlight the need to consider program and systematic challenges to implementation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".