South Eastern Interprofessional Collaborative Learning Environment (SEIPCLE): Nurturing Collaborative Practice
Bibliographic record
Abstract
AbstractBackground: There has been tremendous pressure on Canada’s healthcare system to respond to the increasingly complex health needs of the population despite worsening constraints in financial and human resources. Interprofessional collaborative practice has been seen as an enabler for improving patient care and meeting the current demands on the healthcare system.Methods: The South Eastern Interprofessional Collaborative Learning Environment (SEIPCLE) project, funded by HealthForceOntario, focused on the development and evaluation of the collaborative practice care model in three clinical settings in Southeastern Ontario, Canada. The project was exploratory in nature and used a quasi-experimental design with pre- and post-tests matched with non-equivalent control groups. Several different measures were used, including the Collaborative Practice Assessment Tool (CPAT), an Interprofessional Clinical Education Survey, and a Patient Participation Survey. Quantitative outcome measures were derived from these instruments using factor analysis, and analyzed using regression modelling with co-variates. Focus groups, interviews, and questionnaires provided qualitative data that was coded conceptually and used to complement the results of analyses using quantitative measures. Intervention teams participated in educational components that addressed identified weaknesses in their collaborative practice. Educational components included online modules, workshops, and real-time activities.Findings: Implementation of educational components in the clinical setting posed a number of challenges to reducing the exposure time for some of the intervention teams. Barriers to and enablers of the development of collaborative practice in the healthcare system were identified.Conclusion: Overall, all three intervention teams demonstrated an increase in perceived levels of collaborative practice. Although the results were not statistically significant, the effect, size, and magnitude of change were considered substantial.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".