Regionalization in PEI Seven Years Later: <i>Integrated Health and Social Services Bring Teamwork and Improved Care</i>
Bibliographic record
Abstract
A health services leader in Prince Edward Island, Kenneth Ezeard, CHE, believes that sometimes small can be better, especially when it comes to breaking down barriers between different segments of health and social services. He hopes that PEI can provide ideas and models for his peers across the country, even those in large urban centres. Mr. Ezeard has more than 30 years of experience in health administration. Before joining PEI's West Prince Health Authority as CEO, he was administrative services director for the PEI Health and Community Services Agency. For 16 years before that, Mr. Ezeard was executive director of the Queen Elizabeth Hospital in Charlottetown. He is the current chair of the Canadian College of Health Service Executives (CCHSE), and a former chair of the Canadian Council on Health Services Accreditation (CCHSA) and Canadian Healthcare Association (CHA). In this interview, Mr. Ezeard reflects on the role of national organizations in healthcare, as well as on how regionalization has benefited the residents of his province.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".