La régionalisation à l'Î.-P.-É. Sept ans plus tard: <i>le travail d'équipe et des soins améliorés grâce à l'intégration de la santé et des services sociaux</i>
Bibliographic record
Abstract
Un dirigeant des services de sante a l’Ile-du-Prince-Edouard, Kenneth Ezeard, CHE, estime qu'il est exact d'affirmer que la petite taille d'un organisme peut parfois etre avantageuse, surtout quand vient le temps de faire tomber les barrieres qui se dressent entre les differents segments des services sociaux et des services de sante. Il espere que le modele de l’I.-P.-E. pourra inspirer ses pairs ailleurs au pays, meme dans les plus grands centres urbains. M. Ezeard compte plus de 30 ans d'experience en administration de la sante. Avant de prendre les renes du West Prince Health Authority (I.-P.-E.) a titre de p.d.g., il a occupe les fonctions de directeur des services administratifs du PEI Health and Community Services Agency. Precedemment, il a ete directeur general du Queen Elizabeth Hospital de Charlottetown pendant 16 ans. Il est president du conseil d'administration du College canadien des directeurs de services de sante (CCDSS) et a deja cumule les memes fonctions aupres du Conseil canadien d'agrement des services de sante (CCASS) et de l'Association canadienne des soins de sante (ACS). Dans cette entrevue, M. Ezeard parle du role que jouent les organismes nationaux dans les soins de sante et des effets positifs de la regionalisation pour les citoyens de sa 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 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".