Characteristics, barriers and facilitators of initiatives to develop interprofessional collaboration in rural and remote primary healthcare facilities: a scoping review
Bibliographic record
Abstract
INTRODUCTION: Despite strong evidence supporting interprofessional collaboration (IPC) and the documented need for collaborative practice in primary health care (PHC), initiatives to promote IPC in rural and remote PHC facilities have not been extensively studied. The purpose of this article is to map interprofessional education (IPE) and interprofessional practice (IPP) initiatives implemented to promote IPC in rural and remote PHC facilities, and identify barriers and facilitators to their implementation. METHODS: A scoping review was conducted. After two reviewers filtered titles and abstracts, 94 retained articles were subsequently screened. Finally, 23 articles were selected and analyzed using a directed content analysis approach in NVivo v12. RESULTS: Only 10 articles focused on the implementation of initiatives to improve IPC, while the majority reported barriers and facilitators. The most common IPE initiatives were workshops, courses, discussion groups and simulations, while IPP initiatives fell into two main categories: clinical or technological tools. Limited human resources, understanding of roles, and knowledge of context as well as traditional roles, were identified as barriers. Team size, past experience and relationships, connection to community, flexibility and openness, and financial support were facilitators to developing IPC. CONCLUSION: Deployment of IPC in rural and remote PHC facilities is critical given the various challenges faced in these clinical settings. The facilitators identified in this literature review are specific to rural and remote clinical settings and provide hope that new initiatives more tailored to rural and remote settings will be implemented and evaluated in the future to improve IPC and care delivery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.037 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".