The Teachers’ Role in the Assessment of Selective Mutism and Anxiety Disorders
Bibliographic record
Abstract
Selective mutism (SM) is a childhood disorder characterized by failure to speak in social situations, despite there being an expectation to speak and the capacity to do so. There has been a focus on elucidating the differences between SM and anxiety disorder (ANX) in the recent literature. Although children with SM exhibit more symptoms at school than at home, the assessment of SM typically does not involve teacher reports. There is also a lack of research to help us better understand how to best support students with SM in the classroom, and linking assessment to intervention. The Teacher Telephone Interview: Selective Mutism and Anxiety in the School Setting (TTI-SM) was developed by a group of researchers across three large children’s hospitals in Canada, within specialized ANXs Clinics, with the goal of addressing several of these diagnostic and treatment issues. Child participants (ages 6-11) were referred for SM ( n = 19) or ANX ( n = 10). Findings revealed that the SM subscale of the TTI-SM has acceptable psychometric properties. Scores on the SM subscale between the two groups of children were statistically significant t(29) = −3.67, p < .001, η 2 = .33, suggesting that the SM subscale was able to distinguish between children with SM and ANX. Given the promising findings, and possible uses of this tool for assessment and intervention, the TTI-SM warrants further research. The role of the teacher in the assessment of children with SM and anxiety disorders, and future directions are discussed.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".