An Overview of Reviews on Interprofessional Collaboration in Primary Care: Effectiveness
Bibliographic record
Abstract
INTRODUCTION: Interprofessional collaboration (IPC) is increasingly used but diversely implemented in primary care. We aimed to assess the effectiveness of IPC in primary care settings. METHODS: An overview (review of systematic reviews) was carried out. We searched nine databases and employed a double selection and data extraction method. Patient-related outcomes were categorized, and results coded as improvement (+), worsening (-), mixed results (?) or no change (0). RESULTS: 34 reviews were included. Six types of IPC were identified: IPC in primary care (large scope) (n = 8), physician-nurse in primary care (n = 1), primary care physician (PCP)-specialty care provider (n = 5), PCP-pharmacist (n = 3), PCP-mental healthcare provider (n = 15), and intersectoral collaboration (n = 2). In general, IPC in primary care was beneficial for patients with variation between types of IPC. Whereas reviews about IPC in primary care (large scope) showed better processes of care and higher patient satisfaction, other types of IPC reported mixed results for clinical outcomes, healthcare use and patient-reported outcomes. Also, reviews focusing on interventions based on pre-existing and well-defined models, such as collaborative care, overall reported more benefits. However, heterogeneity between the included primary studies hindered comparison and often led to the report of mixed results. Finally, professional- and organizational-related outcomes were under-reported, and cost-related outcomes showed some promising results for IPC based on pre-existing models; results were lacking for other types. CONCLUSIONS: This overview suggests that interprofessional collaboration can be effective in primary care. Better understanding of the characteristics of IPC processes, their implementation, and the identification of effective elements, merits further attention.
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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 | 0.031 | 0.116 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.029 | 0.026 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".