Lack of Publicly Available Documentation Limits Spread of Integrated Care Innovations in Canada
Bibliographic record
Abstract
As healthcare in Canada is provincially operated, the program innovations in one jurisdiction may not be readily known in other jurisdictions.We examine the availability of implementation-specific data for 30 innovative Canadian programs designed to integrate health and social services for patients with complex needs.Using publicly available data and key informant interviews, we were able to populate only ~50% of our data collection tool (on average).Formal program evaluations were available for only ~30% of programs.Multiple barriers exist to the compilation and verification of healthcare programs' implementation data across Canada, limiting cross-jurisdictional learning and making a comparison of programs challenging. RésuméÉtant donné que les soins de santé au Canada sont administrés par les provinces, les innovations présentes dans une administration peuvent passer sous le radar des autres administrations.Nous examinons la disponibilité des données concernant la mise en œuvre de 30 programmes canadiens novateurs conçus pour intégrer les services de santé et les services sociaux à l'intention des patients ayant des besoins complexes.À l' aide des données publiquement accessibles et d' entrevues avec des informateurs clés, nous n' avons pu remplir qu' environ 50 % de notre outil de collecte de données (en moyenne).Les évaluations officielles des programmes n'étaient disponibles que pour environ 30 % d' entre eux.Il existe de nombreux obstacles à la compilation et à la vérification des données sur la mise en œuvre des programmes de soins de santé au Canada, ce qui limite l' apprentissage entre les administrations et complique la comparaison entre les programmes.
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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 | 0.033 | 0.146 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.023 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".