Adopting OurNotes in Canadian Mental Health Settings: Implementation Recommendations
Bibliographic record
Abstract
The OurNotes movement aims to support patient collaboration and engagement in care through the implementation of pre-visit notes. By contributing to a pre-visit history or agenda, the patient voice is incorporated into the visit. While OurNotes has been successfully piloted in primary and acute care settings, its implementation in Canadian mental healthcare settings has been limited. In this study, we conducted semi-structured interviews with patients, care partners and mental health clinicians to identify implementation recommendations for OurNotes in Canadian mental health contexts. Six recommendations were identified. These recommendations can be adopted by organizations considering the implementation of OurNotes in mental health clinical settings.
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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 | 0.100 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".