Empowerment through Mobile Apps: A Mixed Methods Case Study of an Application for Pregnant Women
Bibliographic record
Abstract
This article analyses the manner in which mobile health applications contribute to the empowerment of patients. The theoretical background included m-health, user empowerment and value co-creation. Quantitative and qualitative methods were used to investigate the representative case study of the Kangaroo application, a free app designed by a health-tech start-up and a reference Brazilian hospital which seeks to make Brazilian women empowered for a healthy pregnancy. The data collection effort comprised application log analysis of 6 months of records of almost one-hundred thousand users, a mobile-based survey with approximately 400 women, and 16 in-depth interviews. The results demonstrated that the app’s social features impacted more than personal features. This research’s results suggested that the perception of patient empowerment is not greater for the active users of the application, so the patient is not related to ‘doing’, but rather to ‘being able to do’. The participation of specialised professionals, who moderate and interact with the app community, is valued by the users and mentioned as a differential among other health information sources available on the Web. This study was approved by the Brazilian Research Ethics Committee.
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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 | 0.006 | 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 | 0.001 |
| 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".