Understanding how behaviour therapists use autism spectrum disorder diagnostic information for intervention planning
Bibliographic record
Abstract
Understanding how behaviour therapists incorporate diagnostic assessments into their intervention planning can help to streamline assessment procedures and facilitate communication. The objectives are to identify what information from the diagnostic assessment is received by behaviour therapists and which assessment elements are most important and relevant for treatment planning. Behaviour therapists, identified through Ontario registries, were surveyed about their use of diagnostic information in treatment planning. Seventy-one behaviour therapists completed the survey (response rate = 35.5%). The diagnostic information most frequently received by respondents included brief (69%) and detailed (49.2%) physician/psychologist report, speech/language assessment report (52.1%) and individualised education plan (50.7%). Most respondents indicated that information from the physician/psychologist report is often out-dated (74.6% Agree/Strongly Agree). There was variable agreement that the information in the diagnostic package influences the type and quantity of treatment. These findings demonstrate that while diagnostic assessments received by behaviour therapists are important to their planning, other independently obtained sources of information, such as client interviews, are relatively more important to this process. The diagnostic assessment is one tool to inform treatment planning; however, up-to-date information about the child's needs is likely to be more informative.
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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 | 0.029 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".