Applying Theory-Driven Approaches To Predicting Pediatric Mental Health Clinician Behavior In The Utilization Of Evidence-Based Practice
Bibliographic record
Abstract
Within pediatric mental health, of the only 25% of children with emotional and behavioral disorders that receive mental health services many receive treatments and interventions that are not based upon evidence. The question remains how to support mental health clinicians to utilize the evidence we have regarding the treatment of pediatric mental health disorders. Research findings consistently demonstrate that there are a variety of successful interventions which can be effective in changing clinical behaviors. However, further research is required to develop and validate a coherent theoretical framework of health professional behavioral change to better inform the choice of interventions. This study applied theory-driven approaches to predict pediatric mental health clinician behavior in the utilization of evidence-based practice. A national web based survey of pediatric outpatient mental health clinicians (N=154) applying the Theory of Planned Behavior and Operant Learning Theory (Habit and Reinforcement) was conducted. The clinical behaviors of interest were: 1) Recommendation of medication consultation/prescription for the treatment of ADHD; 2) Recommendation of parent training regarding child behavior management; and 3) Utilization of evidence-based group therapy with the specific objective of reducing wait lists. Behavioral intention, a theoretically derived measure, was the main outcome measure. Habit uniquely accounted for 61%, 20% and 25% of the variance, respectively in the three behaviors of interest (parent management, medication, and group treatment for waitlist management). Attitude uniquely contributed a further 5% of the variance in intention in medication consultation/prescription while Reinforcement uniquely explained an additional 10% of the intention to use group treatment. Habit is the single greatest predictor of pediatric mental health clinician behavioral intention in the utilization of evidence-based practice. Habit describes why clinicians are engaging in a behavior (it is what they’ve always done), but the other theoretical predictors tell us something about how to change this habit.
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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 | 0.024 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".