Perceived Need and Social Relatedness Contribute to Change in Selective Prevention for Mental Illness: a Mixed Methods Study
Bibliographic record
Abstract
Selective preventive interventions aim to reduce mental illness in high-risk populations, yet the reasons why some children benefit while others do not remain unclear. This study explores participants' perceptions of mechanisms contributing to change in a family-based preventive intervention for children of parents with severe mental illness. Using an exploratory sequential mixed methods approach, we conducted an abductive qualitative analysis of focus groups (eight parents, eight children) to identify narratives of mechanisms contributing to change. The qualitative findings informed a subgrouping variable for a quantitative post hoc exploratory subgroup analysis of secondary data from the VIA Family trial (N = 110). The qualitative findings indicate that child mental health problems, parents' personal unmet needs from childhood, children's relatedness to peers and family, and contextual family-focused activities contribute to change within selective prevention. Quantitative results indicated that parents motivated by a need for support at baseline exhibited meaningful improvements in the home environment upon enrollment in the experimental preventive intervention compared with families motivated to support science (mean change: 5.07, 95% CI 2.11 to 8.03). However, no significant subgroup differences were observed in changes in children's global functioning between the allocation groups. Parents' perceived need for support facilitated engagement and home improvements, while children's relatedness to peers and family contributed to their intervention experiences. These findings emphasize the importance of motivation and social connections in intervention outcomes, contributing to the growing field of precision prevention. Future research should explore these mechanisms as potential mediators or mechanisms of action for selective prevention. ClinicalTrial.gov Identifier: NCT03497663.
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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 | 0.039 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".