From Crisis to Recovery: Mental Health Service Demand in Alberta, Canada — A Policy Analysis with Illustrative Supply–Demand Modeling (2023–2024)
Bibliographic record
Abstract
This project is a document-based policy analysis of Alberta’s publicly funded mental health and addiction system response during the 2023–2024 fiscal year, developed to clarify how a publicly financed, zero-copayment system adjusts when help-seeking rises during a system shock. Using publicly available, aggregate, and non-identifiable sources—primarily the Government of Alberta’s Mental Health and Addiction Annual Report (2023–2024)—we extracted reported program implementation and reach/activity metrics for major initiatives (e.g., CASA Mental Health classrooms and core youth services, the Virtual Opioid Dependency Program, recovery communities, and Counselling Alberta/tele-mental health). We summarize funded/planned expansions alongside reported activity using transparent, program-specific units as presented in the source materials (e.g., clients, admissions, sessions, beds/sites), and we clearly distinguish empirical reporting from modeled outputs. To support the interpretation of system-level dynamics, we also include an illustrative supply–demand simulation calibrated to reported trends in utilization (including evidence on youth mental health care use). The simulation is explicitly mechanism-based rather than causal or predictive: it uses a unit-cost proxy solely for visualization in a zero-copayment setting. It computes delivered volume under a capacity constraint, treating unmet demand as queueing/congestion when modeled demand growth exceeds deliverable capacity. Deterministic one-way sensitivity analysis and simple multi-way scenarios are provided to show how key assumptions (baseline utilization, demand increase, capacity expansion) affect modeled delivered volume and implied unmet demand, consistent with good modeling and reporting practice. Expected outcomes are (1) a reproducible empirical summary of Alberta’s 2023–2024 publicly reported mental health and addiction program activity and funded expansion signals; (2) a straightforward analytic narrative that separates observed reporting from illustrative modeling; and (3) policy-relevant implications focused on workforce stabilization, digital equity, culturally safe and navigable pathways, and performance monitoring so that capacity expansion translates into equitable access. All supporting materials in this OSF registration include the README, extraction tables, the parameter/assumption table with bounds and source attribution, analysis workbook(s), figure files, and brief reproduction instructions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".