Development of an Accessible Screening Tool for the Assessment of ADHD among Classroom Students
Bibliographic record
Abstract
A screening tool which teachers can use to assess whether students in their classroom are likely to have attention deficit disorder (ADHD) will be developed and tested, for the purpose of streamlining the process of recommending a child for clinical assessment. Other instruments for recommending a child for clinical diagnosis (Connors Rating Scale), or for formally diagnosing a child (ADHD Rating Scale) are examined. Public elementary school teachers in Kingston (n=50) will be presented with vignettes describing the behaviors of three students; one with inattentive ADHD, another with hyperactive ADHD, and a control without ADHD. The teachers will first perform an informal analysis of each student, using personal experiences and previous training to assess each child, without the use of the screening tool. They will not be informed about the nature of the study. The participants will then be asked to assess the same students using the screening tool. The differences in accuracy between both assessments of each child in the vignettes will be measured. Practical applications of this instrument include cost effectiveness, the ability for teachers
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".