Translating Best Practices into Service: Implementing Early Intervention for Psychosis across Canada
Bibliographic record
Abstract
<p>Canada is a large, diversely populated country with numerous early intervention (EI) services that have unique characteristics. Many challenges have made the implementation of such services an interesting process that can inform service delivery and program development. Families, consumers, and non-profit organizations have played pivotal roles in advocating for provincial funding of EI services. Many provinces have developed policies for the roll-out of EI services within their jurisdiction, policies that reflect the key principles of the EI paradigm. Canadian centers have developed internationally recognized research programs on first-episode psychosis and EI. Although many provinces have adapted best practice guidelines, many differences exist within and between provinces in the delivery of EI services. Upholding the tenants of EI, while optimizing resource allocation across wide distances and varied populations, remains a challenging balancing act. Yet this task is well worthwhile, given the potential for positive impact by providing timely and high-quality care to young people and their families who are coping with the early stages of psychosis. This overview summarizes the translation of best practices into service delivery for EI programs across Canada. </p> <h4>ABOUT THE AUTHORS</h4> <p>Suzanne Archie, MD, FRCP(C), is Associate Professor, Department of Psychiatry and Behavioural Neurosciences, McMaster University, and Clinical Director, Cleghorn Program, St. Joseph’s Healthcare Hamilton, Ontario, Canada. Heather Hobbs, RN, BScN, CBSDipl, Care Coordinator, Cleghorn Program, St. Joseph’s Healthcare Hamilton. Natasja Menezes, MDCM, MSc, FRCP(C), is Assistant Professor, Department of Psychiatry and Behavioural Neurosciences, McMaster University, and is Psychiatrist, Cleghorn Program, St. Joseph’s Healthcare Hamilton. </p> <p>Address correspondence to: Suzanne Archie, MD, FRCP(C), 703-25 Charlton Ave. E., Hamilton, ON L8N 1Y2 Canada; fax 905-525-2805; or e-mail <a href="mailto:jbuffet@mcmaster.ca">jbuffet@mcmaster.ca</a>. </p> <p>Dr. Archie, Ms. Hobbs, and Dr. Menezes have disclosed no relevant financial relationships.</p> <p>The authors would like to thank Julie Buffet for all of her assistance in preparing this article. They would also like to thank and acknowledge the following people for the information they provided about early intervention services: Amal Abdel-Baki, Don Addington, Terry Bedard, Gretchen Conrad, Mirella Fata, Tom Hall, Laura Hansen, L. Hanson, Janice Harris, Bill Honer, Cathy Kavanagh, Annette King, Christina Krack, Gord Langill, Elizabeth Lines, Eric Macnaughton, Karen O’Connor, Kola Oyewumi, Bonnie Pape, Karen Tee, John Trainor, and Cheryl Zufelt.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".