An Overview of Reviews on Interprofessional Collaboration in Primary Care: Barriers and Facilitators
Bibliographic record
Abstract
INTRODUCTION: Interprofessional collaboration (IPC) is becoming more widespread in primary care due to the increasing complex needs of patients. However, its implementation can be challenging. We aimed to identify barriers and facilitators of IPC in primary care settings. METHODS: An overview of reviews was carried out. Nine databases were searched, and two independent reviewers took part in review selection, data extraction and quality assessment. A thematic synthesis was carried out to highlight the main barriers and facilitators, according to the type of IPC and their level of intervention (system, organizational, inter-individual and individual). RESULTS: Twenty-nine reviews were included, classified according to six types of IPC: IPC in primary care (large scope) (n = 11), primary care physician (PCP)-nurse in primary care (n = 2), PCP-specialty care provider (n = 3), PCP-pharmacist (n = 2), PCP-mental health care provider (n = 6), and intersectoral collaboration (n = 5). Most barriers and facilitators were reported at the organizational and inter-individual levels. Main barriers referred to lack of time and training, lack of clear roles, fears relating to professional identity and poor communication. Principal facilitators included tools to improve communication, co-location and recognition of other professionals' skills and contribution. CONCLUSIONS: The range of barriers and facilitators highlighted in this overview goes beyond specific local contexts and can prove useful for the development of tools or guidelines for successful implementation of IPC in primary care.
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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 | 0.125 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.031 | 0.033 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".