Interprofessional collaboration between general practitioners and primary care nurses in Belgium: a participatory action research
Bibliographic record
Abstract
Given the sociodemographic challenges facing the Belgian primary care system, it is essential to strengthen interprofessional collaboration (IPC) between healthcare providers. Therefore, our aims for this study were to assess IPC between general practitioners (GPs) and nurses; identify target priorities for improving IPC; and facilitate the planning and implementation of the proposed improvement strategies. Based on diversity criteria, six groups of GPs and nurses were chosen for a participatory action research. Participants performed a SWOT analysis of their IPC to identify strengths and weaknesses of their collaboration practice configurations. Main factors limiting IPC were related to the type of financing system which impeded or facilitated multidisciplinary team meetings, a weak functional integration, and a lack of interprofessional education. Overall, communication and task delegation were co-identified as common priorities. Actions prioritized by each group were related to these two priorities and accounted for local, specific needs. Communication could be supported through improved tools and dedicating time for multidisciplinary team meetings. Task delegation was more challenging and raised questions related to nurses’ training, legislation, and payment systems. IPC seems to be easier to achieve when healthcare professionals belong to the same organization and consider themselves a team.
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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".