Transformation of primary care settings implementing a co-located team-based care model: a scoping review
Bibliographic record
Abstract
BACKGROUND: In Canada, primary care reforms led to the implementation of various team-based care models to improve access and provide more comprehensive care for patients. Despite these advances, ongoing challenges remain. The aim of this scoping review is to explore current understanding of the functioning of these care models as well as the contexts in which they have emerged and their impact on the population, providers and healthcare costs. METHODS: The Medline and CINAHL databases were consulted. To be included, team-based care models had to be co-located, involve a family physician, specify the other professionals included, and provide information about their organization, their relevance and their impact within a primary care context. Models based on inter-professional intervention programs were excluded. The organization and coordination of services, the emerging contexts and the impact on the population, providers and healthcare costs were analysed. RESULTS: A total of 5952 studies were screened after removing duplicates; 15 articles were selected for final analysis. There was considerable variation in the information available as well as the terms used to describe the models. They are operationalized in various ways, generally consistent with the Patient's Medical Home vision. Except for nurses, the inclusion of other types of professionals is variable and tends to be associated with the specific nature of the services offered. The models primarily focus on individuals with mental health conditions and chronic diseases. They appear to generally satisfy the expectations of the overarching framework of a high-performing team-based primary care model at patient and provider levels. However, economic factors are seldom integrated in their evaluations. CONCLUSIONS: The studies rarely provide an overarching view that permits an understanding of the specific contexts, service organization, their impacts, and the broader context of implementation, making it difficult to establish universal guidelines for the operationalization of effective models. Negotiating the inherent complexity associated with implementing models requires a collaborative approach between various stakeholders, including patients, to tailor the models to the specific needs and characteristics of populations in given areas, and reflection about the professionals to be included in delivering these services.
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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 | 0.020 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.014 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".