Data Byte: An Insight on Fetal Alcohol Spectrum Disorder and Educational Achievement
Bibliographic record
Abstract
IntroductionData Bytes are bite-sized pieces of data from the longitudinal project, Experiences of Alberta Children and Youth Over Time, 2005-06 to 2010-11. To promote public engagement, the Data Byte series is designed to highlight a finding from a full report to give readers a quick data morsel to chew on. Objectives and ApproachFetal Alcohol Spectrum Disorder (FASD) is a complex disorder caused by alcohol exposure during pregnancy. To understand how young people with FASD in Alberta are performing in schools, individually linked administrative data were used from 2005/06 to 2010/11. An individual was considered to have FASD if, at any point in the 6 years, they received a diagnostic code for fetal alcohol syndrome, or for being newborn affected by maternal use of alcohol (ICD 10-CA Q86.0 or P04.3) from a health service (emergency department or ambulatory care visit, or a hospitalization), or from a child disability service program. ResultsOf young Albertans with FASD, almost 40% were meeting or exceeding educational expectations compared to 80% of young Albertans without a diagnosis of FASD. Expectations for educational achievement were computed by Alberta Education using age, grade, school type, special education codes, provincial achievement test scores, home education status, number of high school credits earned, number of higher level courses taken, average grade in higher level courses, possession of an Alberta Education certificate or diploma, and Alexander Rutherford scholarship eligibility. Conclusion/ImplicationsData Bytes are designed to encourage stakeholders to explore: Is the definition of “meeting educational expectations” used for the general population appropriate for a population with complex needs? What other outcomes might be used to determine whether services are improving the quality of life for young Albertans with FASD?
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".