Understanding policy supports for Team-Based primary healthcare models.
Bibliographic record
Abstract
Background: Industrialized countries are facing pressure to address the complexity of populations who experience high needs with significant costs to the health system. Interprofessional team-based care (TBC) is an essential component of integrated care strategies to respond to these complex needs. However, TBC models operate within complex policy environments that can facilitate or constrain their activities. The main goal of this project is to develop deeper insights on the manner in which system-level policies contributed in shaping TBC in three Canadian provinces - Ontario (ON), British Columbia (BC) and Nova Scotia (NS). Approach: This research consisted of an analysis of provincial policy documents (3 ON, 7 BC and 4 NS). Data was extracted using an innovative framework, based on a concept mapping exercise completed by the research team. Qualitative description and matrix comparative analysis for similarities and differences were used for data analysis. Results: Across the provinces, government investments encouraged the development of various models of TBC models (physician clinics, interprofessional team clinics, nurse practitioner led clinics, Community Health Centers). Government support was often tied to changes in the operations of TBC models such as increasing the number of services or extending opening hours. To enhance TBC, some policies expanded the roles and competencies of allied health professionals or shifted services from hospital to community settings. Patient engagement and participation in policy development and implementation was more salient in BC policies than ON. Implications: Provincial policymakers play a key role in creating the conditions in which TBC models are created and operate. This policy analysis highlights successful strategies that can contribute to improving the development and operations of TBC. These findings can inform the development of public polices and avenues for adopting practices so as to foster TBC in Canada.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.018 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".