Health Technology Access and Peer Support Among Digitally Engaged People Experiencing Homelessness: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Although the effects of digital health are receiving wide scientific attention, very little is known about the characteristics of digitally engaged people experiencing homelessness, especially in Central and Eastern Europe. Our previous research revealed a considerable level of internet use in the homeless population of Budapest, Hungary, for general purposes (350/662, 52.9%) and medical purposes (229/664, 34.6%). Moreover, a digitally engaged subgroup was identified (129/662, 19.5%). OBJECTIVE: The aim of this exploratory study was to map out the resources, attitudes, and behaviors of digitally engaged homeless individuals in relation to digital technology to set the basis for potential health policy interventions, which will enable better access to health services through strengthening of the digital components of the existing health care system. METHODS: Between August 18, 2022, and October 27, 2022, a total of 12 in-depth semistructured interviews were conducted in 4 homeless shelters in Budapest, Hungary. Upon analysis by 3 independent evaluators, 2 interviews were excluded. The interviewees were chosen based on purposive sampling with predefined inclusion criteria. Thematic analysis of the transcripts was conducted. RESULTS: In the thematic analysis, 4 main themes (attitude, access, usage patterns, and solutions for usage problems) emerged. Health-related technology use mostly appeared in health information-seeking behavior. Online search for prescribed medications (5 interviews), active ingredients of medications (4 interviews), medicinal herbs believed to replace certain pills (2 interviews) or foods, and natural materials (1 interview) were mentioned. Moreover, mobile health app use (3 interviews) was reported. The intention to circumvent or check on mainstream health care solutions was mainly associated with previous negative experiences in the health care system. Several gaps in the daily use of technology were identified by the interviewees; however, more than half of the interviewees (6/10) turned out to be contact points for their peers for digital problem-solving or basic digital literacy skill enhancement in the homeless shelters. Furthermore, a lack of institutional support or special programs targeting senior clients was noted. CONCLUSIONS: Digitally engaged homeless individuals might become mediators between their peers and comprehensive digital health programs. They have the trust of their peers, can recognize and harness the benefits of digital technology, and are able to provide meaningful help in technology- and usage-related issues through experience. Digital health services have great promise in community shelters for managing and preventing health issues, and digitally engaged individuals might be important for the success of such services.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".