A Teacher’s Checklist for Evaluating Treatment Intrusiveness
Bibliographic record
Abstract
Teachers are frequently involved in developing and evaluating treatments for problematic behaviors. Along with other members of the interdisciplinary team, they must determine the level of intrusiveness that a treatment may have on a student. Several factors that influence the intrusiveness of treatment procedures are described. These factors were used to develop a checklist that could be used systematically by teachers to evaluate the intrusiveness of treatments recommended by treatment teams. After the checklist was administered to a group of preservice teachers, it was found to be capable of discriminating among several treatment options described in a series of case vignettes. The implications of incorporating such a checklist into the design and implementation of treatments for problem behavior are discussed.Les enseignants sont souvent impliqués dans le développement et l’évaluation de traitements des problèmes de comportement. De concert avec d’autres membres d’une équipe interdisciplinaire, ils doivent déterminer dans quelle mesure un traitement est intrusif pour l’élève. L’article décrit plusieurs facteurs qui influencent le degré de discrétion des procédures. À partir de ces facteurs, on a dressé une liste de vérification dont pourraient se servir les enseignants de façon systématique pour évaluer à quel point les procédures recommandées par les équipes de traitement sont intrusives. La liste a été présentée à un groupe de stagiaires et s’est avérée capable de distinguer plusieurs options de traitement décrites dans une série de vignettes d’étude de cas. S’ensuit une discussion portant sur les conséquences d’incorporer une telle liste de vérification dans la conception et la mise en œuvre de traitements pour les problèmes de comportement.
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".