Coping Power at the REACH School Network: A pilot feasibility study
Bibliographic record
Abstract
Objectives: School-based health centres (SBHCs) provide developmental and mental health care to children with socioeconomic disparities. We piloted a validated behavioural intervention called Coping Power (CP) for children with disruptive behaviour through our SBHC program. The objective of this pilot study was to examine the feasibility of CP in the SBHC setting. Methods: All parent/caregiver and child dyads enrolled in CP from 2018 to 2019 and 2021 to 2022 were invited to participate in the study. Demographic information and behaviour rating scales were collected at baseline. Feasibility metrics included attendance and satisfaction survey responses. Results: A total of 31 parent/caregiver-child dyads were included. Approximately 40% of families had an annual income of <$49,999. Regarding attendance, 22/31 children/parent/caregiver dyads (70.9%) missed ≤2 sessions. Thirteen parents/caregivers completed the CP satisfaction survey and indicated that they were either 'somewhat' (n = 4) or 'very satisfied' (n = 9) with the program. Of the 18 children who completed the satisfaction survey, 13 (72.2%) shared that either the 'sort of' or 'for sure' group helped them cope with their anger better. Conclusions: This pilot study found that CP delivered within the SBHCs was feasible. Improving access to CP for disadvantaged children may improve mental health 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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".