Using administrative data to examine mental health service use among post-secondary students in Alberta, Canada
Bibliographic record
Abstract
IntroductionPoor mental health among post-secondary students has been on the rise, and as such, has become a growing concern for the Alberta government. Alberta’s major post-secondary institutions have emphasized the need for evidence that would improve mental health supports for students troubled by mental health issues. Objectives and ApproachResponding to the need for evidence, the Child and Youth Data Laboratory profiled the socio-demographic characteristics (sex, socio-economic status, etc) of students who used mental health services between 2005/06 and 2010/11. In addition, using linked administrative data from a range of government programs, the profiles provide new data on the program involvement of post-secondary students who used mental health services, including educational achievement in high school, high cost health service use, the presence of chronic conditions, injury diagnoses, disability status, justice system involvement, income support, and type of mental health condition. ResultsOver the study period, 7% (~6,000) of post-secondary students received mental health services. Of those, between 11 and 13% were high cost health service users, ~20% received an injury diagnosis, and ~15% had a chronic condition. These proportions were higher compared to the proportions among students who did not receive mental health services. Rates of income support service use, corrections involvement, and students with disabilities were higher compared to students not receiving mental health services. A greater proportion of Canadian students (between 6.5% and 7.1%) compared to non-Canadian students (between 3.4% and 4.1%) received mental health services. In 2010/11, a greater proportion of part-time compared to full-time students were diagnosed with an anxiety disorder (3.4%, part-time; 2.3% full-time) or depression (4.0% part-time; 2.3% full-time). Conclusion/ImplicationsEvidence produced from linked administrative data offers a unique understanding of students who use mental health services, particularly in terms of their government program involvement. This new evidence can be used, for example, to determine if mental health service needs are different for Canadian versus non-Canadian students, or for full-time versus part-time students.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".