Commentary on “Video-modelling as an effective solution for coaching carers of autistic adults”: building skills; that should be our priority
Bibliographic record
Abstract
Purpose This paper aims to discuss the importance of offering high-quality support focussed on developing the skills of individuals with intellectual and developmental disabilities. Design/methodology/approach The analysis will be based on the study published by Cohen and McGill (2020), who demonstrated that video modelling led to improvements in support workers’ performance when training adults with intellectual and developmental disabilities to brush their teeth. Findings Developing the skills of staff members and services users should be one of our primary aims. Evidence-based practices grounded in behaviour analysis can help produce optimal outcomes that will improve the quality of service provision and, subsequently, the service users’ quality of life. Originality/value This paper is aimed at parents and professionals working in the field of disabilities who are keen to further improve the service provision of people with disabilities.
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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 | 0.010 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.038 | 0.043 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".