© The Hospital for Sick Children 2003 FETAL ALCOHOL SYNDROME IN THE CANADIAN CORRECTIONS SYSTEM
Bibliographic record
Abstract
Background The availability of services for diagnosis and management of people with Fetal Alcohol Syndrome (FAS), Alcohol Related Neurodevelopmental Disorder (ARND), or Fetal Alcohol Effect (FAE) in the Canadian corrections system is currently unknown. Methods Each province's or territory's corrections system was asked to complete a questionnaire on the demographics of the population and services related to FAS. Responses were obtained from eleven of the thirteen provinces or territories invited to participate. Results The provinces and territories reported a total population of offenders of 148,797. In the eleven responding entities, the mean rate of substance abuse was 50.5%. Of the total population, 13 inmates had a reported diagnosis of FAS for a prevalence rate of 0.087 per 1,000 population. In the Yukon Territory the correction system estimated that 2.6 % of offenders had FAS. None of the entities reported having a screening program for FAS in the corrections system. Three out of eleven entities (27.3%) reported having access to diagnostic services for FAS. The staff training needs reported in this study were very substantial. Interpretation Corrections systems reported few diagnosed cases of FAS and multiple unmet needs to screen, identify, and manage offenders with FAS. Further research is required to identify strategies for low cost expansion of services to screen, identify, and manage offenders with FAS. These studies should also examine the potential impact of these services to increase the success rates of substance abuse treatment, other intervention programs, and the potential to decrease recidivism.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.122 | 0.012 |
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".