Bibliographic record
Abstract
Dear Editor: Disruptive behaviour disorders are major mental health problems that are difficult to treat and costly in terms of suffering, violence, and damaged property. The most common reason for referral to children's mental health services involves disruptive behaviours, that are quite stable from toddlerhood. Intervention may be more effective with younger children, who tend to have less severe behaviour problems; however, there are no evidence-based interventions specifically targeting toddlers (1). Group-based parent training can be effective, cost-efficient, and accessible, so we developed COPEing With Toddler Behaviour to train groups of parents in effective parenting strategies for toddlers (aged 12 to 36 months), using an active learning model (that is, COPE Community Parent Education, 2) that has been used successfully with other age groups. In the development phase of this community-based education group, we focused on enlisting and retaining parents of at-risk toddlers, completing evaluations, identifying any negative impact of the groups, and developing the content in response to feedback. We advertised and offered three 3-session versions of COPEing With Toddler Behaviour in high-risk neighbourhoods. Poverty and associated factors such as poor housing and living in high-crime neighbourhoods have been identified as predictors of disruptive behaviour disorders. To help enlist and sustain group involvement, we provided snacks, childcare, and help with transportation. Each of the 3 weekly sessions were 2 hours in length, held at convenient locations, and led by experienced group facilitators. The groups filled up quickly, and we had a waiting list. Over 3 courses offered, 90 parents registered by telephone, and 48 parents (53%) completed at least 2 of the 3 sessions. All 48 parents completed a client satisfaction questionnaire and reported that they highly valued the course: 88% to 100% said that they learned something new, that they plan to use what they learned, and that they found the course helpful. One-third thought the course was too short. Of the 48 parents, 16 (that is, 62% of the 26 parents we reached by telephone to elicit agreement) completed pretest, posttest, and follow-up standardized parentreport measures (3). Results showed small to medium effect size decreases in difficult toddler behaviour, and dysfunctional parentchild interaction, and parental distress and a significant increase in knowledge of toddler parenting issues. …
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.012 | 0.019 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".