“I think it's something that we should lean in to”: The use of OpenNotes in Canadian psychiatric care contexts by clinicians
Bibliographic record
Abstract
Background: OpenNotes is the concept of patients having access to their health records and clinical notes in a digital form. In psychiatric settings, clinicians often feel uncomfortable with this concept, and require support during implementation. Objective: This study utilizes an implementation science lens to explore clinicians' perceptions about using OpenNotes in Canadian psychiatric care contexts. The findings are intended to inform the co-design of implementation strategies to support the implementation of OpenNotes in Canadian contexts. Method: This qualitative descriptive study employed semi-structured interviews which were completed among health professionals of varying disciplines working in direct care psychiatric roles. Data analysis consisted of a qualitative directed content analysis using themes outlined from an international Delphi study of mental health clinicians and experts. Ethical approval was obtained from the Centre for Addiction and Mental Health and the University of Toronto. Results: In total, 23 clinicians from psychiatric settings participated in the interviews. Many of the themes outlined within the Delphi study were voiced. Benefits included enhancements to patient recall, and empowerment, improvements to care quality, strengthened relational effects and effects on professional autonomy and efficiencies. Despite the anticipated benefits of OpenNotes, identified challenges pertained to clarity surrounding exemption policies, training on patient facing notes, managing disagreements, and educating patients on reading clinical notes. Conclusion: Many benefits and challenges were identified for adopting OpenNotes in Canadian psychiatric settings. Future work should focus on applying implementation frameworks to develop interventions that address the identified challenges.
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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 | 0.025 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.031 | 0.022 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".