Measuring Patient Oriented Outcomes in Children and Youth With Mental Health Concerns: Albertan Key Informant Perspectives
Bibliographic record
Abstract
Mental health concerns among children and youth in Alberta are increasing while poor mental health remains as one of the largest threats to childhood in Alberta. In Canada, mental illness impacts 1 in 4 youth. Demands for mental health services have steadily increased over the past 10 years. To address the child and youth mental health crisis, strategic coordinating and monitoring of child and youth mental health service outcomes are important. This information can inform planning, funding allocation and evaluation. Mental health services were already in crisis when the COVID-19 pandemic hit. The pandemic has only exacerbated the issue, particularly among youth. Delivering supports and services that meet the needs of youth is critical. A better understanding of the efficiency and effectiveness of mental health services is required. Patient-oriented outcome measures are important for gathering information that can incorporate the patient’s own perspectives of their outcomes during treatment. Such measures can inform equitable distribution of funds and efficiency of systems planning. Despite patient-oriented research being a national priority, Canada does not have a policy directing how to conduct patient-oriented research; thus, provinces are creating their own. Alberta lacks a unified approach, resulting in various tools and measures being used, which, has led to issues tracking patient outcomes, identifying trends and referring patients to services. Strategic guidance and policy regarding how to measure and track outcome measures are needed to gather consistent data and provide better services. A lack of consistent data from patient’s perspectives impacts ability to make evidence informed, value-based decisions when allocating funds.
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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 | 0.030 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".