"I'm not alone in that battle": Designing Mobile AR for Mental Health Communication and Community Connectedness
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
For researchers at the intersection of health and human computer interaction, mobile AR presents a compelling platform for public health communication: it is increasingly available, highly customizable, and can present interactive visualizations of complex data. However, designers face challenges not only in adapting appropriate data and relevant public health metrics, but also in assessing their communicative potential and effectiveness for the target community. To contribute insight into this research area, we designed four mobile AR visualizations based on mental health issues and resources for our local university community. We then conducted a mixed-methods field experiment to investigate the impact of our AR visualizations on participants’ awareness and understanding of pressing health issues, and to document barriers to use in this context. We show that our visualizations increased participants’ sense of community connectedness and prompted them to reflect on their relationship with the university community. Based on these findings, we discuss opportunities for the field of human-computer interaction to further support public health communication.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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 it