Recruitment and retention of homeless individuals with mental illness in a housing first intervention study
Bibliographic record
Abstract
BACKGROUND: Homeless individuals with mental illness are challenging to recruit and retain in longitudinal research studies. The present study uses information from the Vancouver site of a Canadian multi-city longitudinal randomized controlled trial on housing first interventions for homeless individuals. We were able to recruit 500 participants and retain large number of homeless individuals with mental illness; 92% of the participants completed the 6-month follow up interview, 84% the 24-month follow up, while 80% completed all follow-up visits of the study. PURPOSE: In this article, we describe the strategies and practices that we considered as critical for successful recruitment and retention or participants in the study. METHODS: We discuss issues pertaining to research staff hiring and training, involvement of peers, relationship building with research participants, and the use of technology and social media, and managing challenging situations in the context of recruitment and retention of marginalized individuals. CONCLUSIONS: Recruitment and retention of homeless participant with mental illness in longitudinal studies is feasible. It requires flexible, unconventional and culturally competent strategies. Longitudinal research projects with vulnerable and hidden populations may benefit from extensive outreach work and collaborative approaches that are based on attitudes of mutual respect, contextual knowledge and trust.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".