Clinician Factors Related to the Delivery of Psychotherapy for Youth with Autism and ADHD
Bibliographic record
Abstract
Children and adolescents with autism are far more likely than children without autism to experience co-occurring mental health problems such as anxiety or depression. Though evidence-based psychotherapy that addresses mental health problems in youth without autism has been effective for youth with autism (e.g., cognitive behaviour therapy), these youth are less likely to receive such interventions. Recent research indicates that clinician factors, such as knowledge, attitudes, confidence, and beliefs, can impact their decisions to provide care, though this work has primarily focused on adults or within the context of one kind of treatment (CBT). The current study examined psychological predictors of clinicians intention to deliver psychotherapy to youth with autism or ADHD. Participants included 611 clinicians aged 20 to 75 across publicly funded agencies in Ontario, Canada. Multiple mediation analyses revealed clinician knowledge was associated with intention to treat clients with autism or ADHD, and normative pressures and attitudes partially mediated this association. Clinicians felt less likely to treat clients with autism than those with ADHD, partly because of differences in their attitudes and normative pressures, which related to their knowledge. This research suggests that targeted training around autism and mental health care may be a useful initiative for mental health agency staff.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".