Perceptions of Service Efficacy Among Young Adults with Childhood Histories of Conduct Problems
Bibliographic record
Abstract
Services developed to address conduct problems in school contexts show limited efficacy. The current study examined how young adults with childhood histories of conduct problems retrospectively understood service efficacy, inefficacy, and what suggestions they had for service improvement. Participants were 41 young adults from Québec (17-21 years old; 53.7% women; 78% white; 77% below the low-income cut-off for single-person households) who had received services for conduct problems starting in childhood. They completed semi-structured interviews about their service usage experiences. Thematic analysis was used to identify how participants discussed themes relating to efficacy, inefficacy, and service improvement. While considerable overlap was observed in how participants and educational professionals understand efficacy (e.g., reduced symptoms) and inefficacy (e.g., worsening symptoms), participants also noted key differences in terms of how they perceived efficacy (i.e., using services to avoid punishment) that are not generally considered by educational professionals. Including user perspectives when assessing service efficacy and inefficacy can provide crucial insight for improving services for youth with conduct problems, providing a starting point for understanding how users evaluate service success, and how they saw services as influencing their psychosocial outcomes.
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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 | 0.003 | 0.007 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".