Successful recruitment strategies for prevention programs targeting children of parents with mental health challenges: An international study
Bibliographic record
Abstract
Research substantiates children of parents with mental disorders including substance abuse face increased risk for emotional and behavioral problems. Although evidence suggests that support programs for children enhance resiliency, recruiting children to these groups remains problematic. This study identifies successful recruitment strategies for prevention programs for children of parental mental illness. The participants were recruited from an international network of researchers. E-mail invitations requested that researchers forward a web-based questionnaire to five colleagues with recruitment experience. Forty-five individuals from nine countries practicing in mental health responded. Descriptive statistics and qualitative content analysis techniques were used. Results: Schools, adult, and youth mental health services were recruitment sources. Nine themes were identified: Relationships, diversified information output, logistics, program consistency, family involvement, recruitment through adults, stigma, recruiting locations, social media. Recruitment barriers were: stigma, inadequate knowledge about parental mental illness and limited time. Transportation to programming was an essential component of successful recruitment.
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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 | 0.071 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".