Retaining a Sample of Homeless Youth.
Bibliographic record
Abstract
OBJECTIVE: Hard to reach populations need to be included in research studies to ensure proper representation of the general population. This paper explores tracking strategies used in the Youth Matters in London project to retain a sample of homeless youth. METHOD: A total of 187 youth, aged between 16 and 24 years, homeless or precariously housed, and experiencing a serious mental health issue were recruited at a community drop-in center, by word of mouth and by snowball sampling. After the initial interview, three repeat interviews were conducted six months apart. RESULTS: The most successful strategy for contacting participants was through a local agency and e-mail. An analysis of the contact data identified participant retention rates as 88%, 86%, and 82% for each successive interview. This longitudinal retention rate is very high compared with research in other vulnerable populations, suggesting a strong willingness to participate in the Youth Matters in London project. CONCLUSIONS: Retaining a sample of homeless youth is difficult, however, with time, patience and effort it has proven possible. This research underscores the importance of relationships with community agencies to retain vulnerable youth samples in longitudinal research designs.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".