Validation of the Hayes Ability Screening Index (HASI) for intellectual disabilities among federal inmates in Quebec
Bibliographic record
Abstract
Background: Researchers and clinicians have stressed the importance of indentifying individuals with an intellectual disability (ID) in the criminal justice system, yet there is a lack of convenient instruments available for early screening of ID by non-professionals.Objectives: The goal of the present study was to assess the predictive validity of a screening measure for ID, the Hayes Ability Screening Index (HASI), in a correctional setting.Methods: This cross-sectional study of 324 male offenders recruited from Quebec's Regional Reception Centre of the Correctional Service of Canada compared results of the HASI with two screening subscales of the Wechsler Adult Intelligence Scale (WAIS-III), the full WAIS-III and with the Adaptive Behavioural Assessment System (ABAS-II).Results: Despite a relatively good result of the ROC analysis (AUC.0.702), consideration of high false negative rates is necessary for interpretation of the results.Results of HASI in predicting WAIS-III (IQ≤70) categorization indicated a high false negative rate (sensitivity 48%, specificity 87%) and also high false negative rate for the prediction of the ABAS-II (sensitivity=30%, specificity=84%) categorization.Conclusions: The results are discussed from a practical point of view in terms of limitations of HASI resulting in many false negatives.However, the importance of such screening tools is highlighted, as is the need for more research at different stages of the criminal justice process.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".