After-School Programs and Children’s Mental Health: Organizational Social Context, Program Quality, and Children’s Social Behavior
Bibliographic record
Abstract
OBJECTIVE: The current study examined associations among organizational social context, after-school program (ASP) quality, and children's social behavior in a large urban park district. METHOD: Thirty-two park-based ASPs are included in the final sample, including 141 staff and 593 children. Staff reported on organizational culture (rigidity, proficiency, resistance) and climate (engagement, functionality, stress), and children's social skills and problem behaviors. Children and their parents reported on program quality indicators (e.g., activities, routines, relationships). Parents also completed a children's mental health screener. RESULTS: A series of Hierarchical Linear Models revealed that proficiency and stress were the only organizational predictors of program quality; associations between stress and program quality were moderated by program enrollment and aggregated children's mental health need. Higher child- and parent-perceived program quality related to fewer staff-reported problem behaviors, while overall higher enrollment and higher aggregated mental health need were associated with fewer staff-reported social skills. CONCLUSIONS: Data are informing ongoing efforts to improve organizational capacity of urban after-school programs to support children's positive social and behavior trajectories.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".