Does interprofessional collaboration between care levels improve following the creation of an integrated delivery organisation? The Bidasoa case in the Basque Country
Bibliographic record
Abstract
Introduction: This article explores the impact of the creation of a new integrated delivery organisation on the evolution of interprofessional collaboration between primary and secondary care levels. In particular, the case of the Bidasoa Integrated Healthcare Organisation is analysed. Theory and methods: The evolution of interprofessional collaboration is measured through a validated Spanish questionnaire, with 10 items and a 5-point Likert scale, based on the D’Amour’s model of collaboration [20]. The final sample included 146 observations (doctors and nurses). Results: The questionnaire identified a significant improvement on the mean scores for interprofessional collaboration of 0.57 points before and after the intervention. A significant improvement was also found in the two dimensions of the measure of interprofessional collaboration used, with the size of the change being higher for the dimension related to the organisational setting (0.63) than for interpersonal relationships (0.47). Conclusions: Before and after the creation of the Bidasoa Integrated Healthcare Organisation, an improvement in the perceived degree of interprofessional collaboration between primary and secondary care levels was observed. This finding supports the benefit of a multilevel and multidimensional approach to integration, as in the described Bidasoa case. Discussion: Results on the two dimensions of the measure of interprofessional collaboration used, seem to point to the longer time required for interpersonal relationships to change, compared to the organisational setting.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".