Evaluation framework for a multi-site practice-based interprofessional education intervention
Bibliographic record
Abstract
The interprofessional literature suggests that there is a lack of evidence of the effectiveness of interprofessional education (IPE) on patient outcomes and critiques the methodology used to determine the evidence. This paper describes and critiques a comprehensive evaluation of a practice-based IPE intervention. The evaluation was challenged by the complexity of the project such as having multiple sites with great variability in settings and participants which required a multifaceted evaluation approach. Rather than reporting evaluation findings, this paper discusses the methodological successes and challenges of the evaluation framework used. The evaluation consisted of four components: process, outcomes, context and systems evaluation. A mixed method approach was used to collect information from a variety of data sources. Each evaluation component captured distinctive but complementary aspects of the intervention, providing a more complete understanding of the intervention. However, challenges also emerged, in particular for the outcomes component. Discussion of the challenges and benefits of each evaluation component are intended to inform future evaluation designs of complex practice-based IP education interventions. Specifically, adding systems concepts into evaluation can strengthen the evidence base of the effectiveness of IP education on IP practice and patient outcomes.
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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 | 0.317 | 0.182 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| 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".