A Study Protocol for the "Practitioner Training in Child and Adolescent Psychiatry" Cluster-randomized Pilot Study.
Bibliographic record
Abstract
BACKGROUND: Primary care providers (PCPs) are increasingly called upon to assist in meeting the growing demand for paediatric mental health care in Canada, yet they report inadequate training and confidence to do so. The Practitioner Training in Child and Adolescent Psychiatry (PTCAP) program was designed to fill this gap by teaching PCPs the skills needed to provide frontline care themselves, particularly in rural/remote regions where specialist resources are limited. This innovative educational intervention may improve paediatric mental health care capacity, but a pilot study is needed. METHODS: We designed a cluster randomized, controlled pilot of PTCAP. Random assignment to intervention or control (treatment-as-usual) will occur at the practice level. Participating PCPs (N=61) at sites randomized to intervention will receive eight hours of training in the use of practice guidelines and brief counseling techniques (i.e., common skills/elements) for addressing diagnosable conditions and more general, transdiagnostic concerns. Mental health care capacity at one-week post-intervention will be the primary outcome, assessed through self-report questionnaires of mental health care confidence, and through a more objective, observational assessment of trained skills. We will also examine retention of these skills at one-month follow-up. We expect use of trained common skills/elements to be associated with better child mental health outcomes on the Strengths and Difficulties Questionnaire (N = 250). DISCUSSION: As one of the first RCTs of its kind in Canada, this study will provide unique, preliminary evidence in regards to the feasibility and efficacy of the PTCAP intervention for enhancing rural, paediatric mental health care capacity.
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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 | 0.052 | 0.045 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.236 | 0.033 |
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".