User Perceptions of an mHealth Medicine Dosing Tool for Community Health Workers
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth) technologies provide many potential benefits to the delivery of health care. Medical decision support tools have shown particular promise in improving quality of care and provider workflow. Frontline health workers such as Community Health Workers (CHWs) have been shown to be effective in extending the reach of care, yet only a few medicine dosing tools are available to them. OBJECTIVE: We developed an mHealth medicine dosing tool tailored to the skill level of CHWs to assist in the delivery of care. The mHealth tool was created for CHWs with primary school education working in rural Mexico and Guatemala. Perceptions and impressions of this tool were collected and compared to an existing paper-based medicine dosing tool. METHODS: Seventeen Partners In Health CHWs in rural Mexico and Guatemala completed a one-day training in the mHealth medicine dosing tool. Following the training, a prescription dosing test was administered, and CHWs were given the choice to use the mHealth or paper-based tool to answer 7 questions. Subsequently, demographic and qualitative data was collected using a questionnaire and an in-person interview conducted in Spanish, then translated into English. The qualitative questions captured data on 4 categories: comfort, acceptability, preference, and accuracy. Qualitative responses were analyzed for major themes and quantitative variables were analyzed using SAS. RESULTS: 82% of the 17 CHWs chose the mHealth tool for at least 1 of 7 questions compared to 53% (9/17) who chose to use the paper-based tool. 93% (13/14) rated the phone as being easy or very easy to use, and 56% (5/9) who used the paper-based tool rated it as easy or very easy. Dosing accuracy was generally higher among questions answered using the mHealth tool relative to questions answered using the paper-based tool. Analysis of major qualitative themes indicated that the mHealth tool was perceived as being quick, easy to use, and as having complete information. The mHealth tool was seen as an acceptable dosing tool to use and as a way for CHWs to gain credibility within the community. CONCLUSIONS: A tailored cell phone-based mHealth medicine dosing tool was found to be useful and acceptable by CHWs in rural Mexico and Guatemala. The streamlined workflow of the mHealth tool and benefits such as the speed and self-lighting were found to be particularly useful features. Well designed and positioned tools such as this may improve effective task shifting by reinforcing the tasks that different cadres of workers are asked to perform. Further studies can explore how to best implement this mHealth tool in real-world settings, including how to incorporate the best elements of the paper-based tool that were also found to be helpful.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".