The use of eHealth interventions among persons experiencing homelessness: A systematic review
Bibliographic record
Abstract
OBJECTIVE: eHealth interventions are being developed to meet the needs of diverse populations. Despite these advancements, little is known about how these interventions are used to improve the health of persons experiencing homelessness. The aim of this systematic review was to examine the feasibility, effectiveness, and experience of eHealth interventions for the homeless population. METHODS: Following PRISMA guidelines, a systematic search of PsycINFO, PubMed, Web of Science, and Google Scholar was conducted along with forward and backward citation searching to identify relevant articles. RESULTS: Eight articles met eligibility criteria. All articles were pilot or feasibility studies that used modalities, including short message service, mobile apps, computers, email, and websites, to deliver the interventions. The accessibility, flexibility, and convenience of the interventions were valued by participants. However, phone retention, limited adaptability, a high level of human involvement, and preference for in-person communication may pose future implementation challenges. CONCLUSIONS: eHealth interventions are promising digital tools that have the potential to improve access to care and service delivery. eHealth interventions are feasible and usable for persons experiencing homelessness. These interventions may have health benefits by augmenting existing services and if implementation challenges are addressed. Further evaluation of the effectiveness of eHealth interventions is needed before widespread implementation. Those with lived experience should also be engaged in developing and evaluating these interventions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".