iCare: Insights from the Evaluation of an App for Managing Stress Among Working-Class Indian Women
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Persuasive Technologies (PTs) are widely used for managing stress and improving well-being. PTs could contribute to the effort toward equality by making mental healthcare more accessible, even among underserved communities. However, most existing persuasive applications (apps) focus on designing for people in developed countries. Therefore, to address this gap, this paper presents the evaluation of iCare, a mobile health (mHealth) app for managing stress and improving well-being among an underserved population—the working-class Indian women. Specifically, we combined the power of mobile health and PTs to design the iCare app. To evaluate the effectiveness of iCare for stress management, 30 participants were recruited to use the app for two weeks and completed a post-test questionnaire about their experience followed by an optional interview with 22 participants to uncover additional insights. Quantitative questionnaire data was analyzed using descriptive and inferential statistics, while qualitative interview data was analyzed using a thematic analysis. Results showed that the iCare app was perceived as highly motivational, persuasive, and useful. Also, results show that using the iCare app brought significant positive changes by helping participants to better manage their stress and anxiety. We contribute to HCI research and practice by offering guidelines and insights for designing technologies for people from underserved communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it