How to make an app-based program work and show how it works
Bibliographic record
Abstract
For digital health interventions, the "gold standard" of evaluating effectiveness is the randomized control trial (RCT). Yet, RCT methodology presents issues such as precluding changes to the technology during the study period as well as the use of study settings that do not reflect "real world" contexts. In this paper, we draw on empirical material from our ethnographic research on an app-based program called HIVSmart!, which is a digital strategy designed to support people in the process of HIV self-testing. We explore how this digital health approach was made to work in different contexts, and what this means for monitoring and evaluation of digital health interventions. Our analysis reveals that making technology which is "ease to use" depends on various user practices developed over time and the multiple, shifting users who define this ease of use. Furthermore, it shows that asking whether an app is "working" provides different answers at different moments, as the technology, as well as the human and physical supports around it, change and adapt over time. Therefore, questions around the feasibility of the technology, as well as ongoing monitoring efforts, need to be developed in a way that capture this fluctuation in the technology and its surroundings. Based on our insights, we contribute to implementation science approaches in the field of digital health by putting forward recommendations regarding how we can assess the effectiveness of digital health tools.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".