Trajectory of service use among young Albertans with complex needs
Bibliographic record
Abstract
IntroductionYouth with complex-needs are vulnerable as a consequence of exposure to social adversity and/or chronic health conditions, and are at a high risk of school failure and justice involvement. Information about the patterns of service use across government sectors that influence the life outcomes of complex-needs youth is unknown. Objectives and ApproachYouth with complex needs often engage with multiple services across multiple government sectors for extended periods of time. Understanding the patterns and trajectory of their service use may inform programs, decision makers and government in the optimal allocation of resources to increase their life outcomes. It may reveal where and when interventions would be most effective to improve the life course for vulnerable youth. In this study, through a unique approach to link over 20 administrative longitudinal datasets and a novel trajectory clustering technique, the patterns of service use among complex-needs young Albertans is revealed and visualized. ResultsA trajectory clustering technique was applied to reveal patterns of service use among complex-needs individuals. Compared to the general population, higher proportions of youth with complex needs lived in low socio-economic neighborhoods, suffered from mental health issues, were high cost health service users, and had lower rates of high school completion. Furthermore, youth having complex needs for a longer period of time and who required multiple complex services in a given year had the poorest outcomes, in terms of high school completion, mental health issues, and other health problems. The majority of complex-needs youth came in contact with services via the education system, followed by child services/welfare. Conclusion/ImplicationsThe trajectories of service use among complex-needs youth reveals that these individuals are primarily identified through education. Consequently, educational supports would best address the development of effective programs including mental health supports and other needs.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".