High school completion of students across multiple inclusive education categories: A longitudinal, population-based analysis from British Columbia, Canada
Bibliographic record
Abstract
A paucity of research explores the high school completion rates and earned credentials among students with various disabilities and learning exceptionalities. We fill this gap by conducting secondary data analyses of a longitudinal, population-based administrative database of individual educational records for 68,815 inclusive education students with assorted categories of support needs (‘designations’), followed longitudinally from Kindergarten (school entry, approximately 5/6 years of age) to Grade 12 (when students typically complete high school). Data spanned 19 years (1999/2000 to 2018/2019, inclusive), and were collected by the British Columbia Ministry of Education and Child Care (BC MEDCC) in Canada, who flagged individual student records with one of 12 designations they annually track for school district funding allotments. Specific objectives were, by designation: (1) to explore the specific high school leaving credential students earned; and (2) to investigate associations among seven sociodemographic predictors (cohort, Ever ESL, gender, number of designations, grade-to-grade transition pace, time to initial designation, and school system) with students’ earned credential. Included are designation-specific descriptive analyses, as well assorted multinomial logistic regressions in which students’ earned credential was regressed onto the sociodemographic variables. Across designations, results showed tremendous variation in students’ earned credential and credential data missingness. This study advances knowledge about the educational journeys and eventual high school completion outcomes of inclusive education students, helps grow the literature on education as a social determinant of health for inclusive education students, and serves to guide inclusive education policy and programs to support students in Canada and elsewhere 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".