Measurement-based care in a Canadian treatment program for first responders, military personnel, and Veterans: A case study
Bibliographic record
Abstract
Introduction: The use of measurement-based care (MBC) can significantly improve outcomes for patients, including symptom reduction, improved functioning, and quality of life. However, existing barriers can lead to low implementation rates, especially among first responders, military personnel, and Veterans (FRMV). Overcoming barriers and identifying ways to measure implementation using aggregate data is the next step in integrating MBC into mental health care. This article outlines the current state of MBC implementation at a mental health and addictions hospital in Canada. Methods: Aggregate data provided by an online MBC platform were used to review clinical outcomes (Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, PTSD Checklist for DSM-5, and Working Alliance Inventory-6) and implementation rates (patient adoption and continuous engagement) of a Guardians program tailored for FRMV and compare these outcomes with benchmarks of Canadian and U.S. organizations. Results: Clinical outcomes and MBC implementation rates for the program exceeded national median benchmarks. Patient adoption and engagement increased between 2022 and 2023 and dropped in 2024. Discussion: Results suggest that the program was aligned with patients' clinical needs and addressed hospital needs for a MBC system because patient adoption and engagement exceeded national benchmarks. The consistent rise in patient participation since the implementation of MBC reflects the system's relative success, although barriers such as limited familiarity and staff capacity likely persist. Future work addressing barriers through organizational training and MBC champions may enhance implementation. The complex treatment needs of FRMV underline the importance of evaluating the integration of MBC into programming for this population.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.018 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".