Collaborating with individuals with lived experience to adapt <scp>CANMAT</scp> clinical depression guidelines into a patient treatment guide: The <scp>CHOICE‐D</scp> co‐design process
Bibliographic record
Abstract
Effective treatment of depression involves collaboration with informed patients and families and appropriate knowledge sharing. We describe here our experience, as a case example, of a collaboration to "translate" a clinical guideline designed for practitioners into an accessible, plainlanguage version that patients and families can use during the care process, both to provide basic educational information and to foster informed discussions with their treatment providers. Content experts in knowledge translation, patient advocacy, patient-oriented research, and psychiatry guided overall project design. Our first step was to identify lived experience writers to join in the codesign and co-writing of the "CHOICE-D Patient and Family Guide to Depression Treatment." A national call for writers attracted 62 applicants, from whom eight individuals with lived experience of depression and writing experience were selected. Individuals subsequently attended a welcoming teleconference, followed by a 1-day workshop designed to provide (a) a detailed overview of the clinician guideline, (b) an opportunity to select what should be included in the Guide, and (c) key principles of knowledge translation/lay writing. Both from the workshop and subsequently through the codesign process, lived experience writers recommended that the Guide address symptoms, effects of illness course on treatment, first-line treatments, safety/side effects, and treatment misconceptions. To promote patient autonomy, question scripts (how and what to ask your treatment provider), self-triaging resources, and treatment selection aids were suggested. Stylistic considerations included use of simple yet hopeful language, brevity, white space, key terms glossary, and graphics. Several strategies were particularly useful to optimize writer engagement in the codesign process: a pre-workshop conference call and circulation of project resources, an in-person workshop to increase content knowledge, structured discussion with co-writers and project leads to develop ideas, and practical training exercises with the provision of feedback. Both during and at the end of the project, writers provided additional recommendations for improving the process, including more in-person meetings, distribution of step-by-step instructions on the writing task, and a key terms glossary of technical terms to support their role. In conclusion, we describe a process with practical tips and reflective feedback on important considerations for engaging persons with lived experience as leaders in the codesign and writing process of lay treatment guidelines. These methods may serve as a model for similar projects in other areas of healthcare.
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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 | 0.020 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".