Prioritizing Patient Perspectives When Designing Intervention Studies for Homeless Older Adults
Bibliographic record
Abstract
Purpose: Medical respite provides postacute care to people experiencing homelessness upon hospital discharge if they are too sick to recover on the streets or in a traditional shelter. The current study examined the feasibility of conducting a study to test the effectiveness of a medical respite intervention for older people experiencing homelessness. Methods: Fifteen patient and 11 provider participants were interviewed between July and November 2018. Results: Participants’ considerations for how to design a program of research included (1) desired qualities of researchers; (2) preferences for study design; (3) mechanisms for participant recruitment and retention; (4) what, where, and how to collect data; and (5) barriers and motivations to participation. Conclusions: Findings from this study build on an emerging research base on how to appropriately engage vulnerable patient groups, including older people experiencing homelessness, in trauma-informed research by including peer researchers on research teams to serve as advisors throughout the research process.
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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 | 0.342 | 0.300 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".