Sociodemographic profiles of high school students across multiple types of special needs and disabilities
Bibliographic record
Abstract
A paucity of population‐based research explores the prevalence and sociodemographic characteristics of high school students with varieties of special needs and disabilities. Utilising a population‐based sample of self‐reported data collected in British Columbia, Canada, we investigated the scope and sociodemographic characteristics of adolescents between and within multiple categories of physical, mental, emotional and behavioural needs – including those with two or more conditions and no conditions. First, we computed the most commonly occurring and least commonly occurring special needs categories. Second, we created profiles of the broad sociodemographic characteristics of adolescents in each special needs category. Finally, we determined whether the profiles indicated statistically significant between‐ and within‐category heterogeneity. We found that over one‐quarter of adolescents had one or more special needs, while nearly three‐quarters of the special needs subpopulation had only three of the nine special needs tracked. Also, whether adolescents with a given special need were compared to those from different categories or those within the same category, there was considerable diversity in their sociodemographic attributes. Our study is one of the first to describe adolescents with special needs in this population‐based fashion. We hope that our findings may guide programme and policy development in British Columbia and around the world.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".