A Cumulative Risks Approach to School Absenteeism: Analyzing the Complex Interplay Between & Within Risk Categories
Bibliographic record
Abstract
While the association between risk factors and school absenteeism has been welldocumented, literature regarding clinically-referred youth remains scarce in a Canadian context.Moreover, as most absenteeism risk factors have been studied independently from one another, less is known about their multilayered contribution.Using a sample of 399 youth (Mage = 10.52;SDage = 3.45) and implementing Kearney9s interdisciplinary model and the cumulative risk model of development, cumulative risk indices across five risk factors were compared: Externalizing Behaviors, Internalizing Behaviors, Psychological Disorders, Developmental Trauma, and Family Issues.Their unique associations with school absenteeism and overall aggregated effect were analyzed through binary logistic regression and multivariable logistic regression, respectively.Results suggested that only an accumulation of internalizing behaviors was significantly related to absenteeism and did not differ across age or sex (adjusted odds ratio[aOR] 1.22; 95% confidence interval [CI], 1.04 3 1.43).Implications and future directions are discussed.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".