431 - Establishing a Canadian National ECHO Educational Program focused on Mental Health of Older Adults
Bibliographic record
Abstract
BackgroundProject ECHO is a virtual, case-based capacity-building education program for healthcare providers. It was developed in New Mexico, USA but, due to its effectiveness, the model has now spread to 40 countries around the globe. Baycrest, the Canadian Coalition for Seniors’ Mental Health and the Canadian Academy of Geriatric Psychiatry collaborated to launch a national ECHO for mental health and aging. This partnership, coordinated by a cross-Canadian Steering Group, allows for broad reach, including registration of learning partners from almost all Canadian provinces and territories. The program was funded by the RBC Foundation.MethodsECHO COE: Mental Health pilot consisted of 2 cycles: 6 weekly sessions focused on broader mental health topics (e.g., delirium, mood disorders)10 weeks with more specific topics (e.g., substance use disorders, sleep disorders)Needs assessments of healthcare providers and older adults informed the program curricula. Evaluation included weekly satisfaction surveys, and pre and post evaluations.ResultsParticipants: 154 healthcare providers participated in the 6-week session39% of registrants were nurses or nurse practitioners, 35% allied health professionals, 14% physicians and 12% others9 out of 10 provinces, 1 territory representedPreliminary findings (based on the first 6 sessions): High overall satisfaction (average of 4.5 out of 5).99% would recommend the program to others67% had already shared information with team members and colleagues.ConclusionA national ECHO program is an effective way to bring together clinicians who work with and are interested in the mental health and wellbeing of older adults for education sessions, collaborative and mutual learning as well as for cross-jurisdictional knowledge transfer. Collaborative, cross-professional learning supports the exchange of best practice in mental health for older adults, supports the development of collegial national professional support and can address health system inequities. An international ECHO through IPA would be an exciting and valuable next step.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".