Evaluation of an interprofessional continuing professional development initiative in primary health care
Bibliographic record
Abstract
INTRODUCTION: Interest in collaborative care approaches and in interprofessional education (IPE) to prepare providers for interprofessional collaboration is increasing and particularly so in the field of primary health care. Although evidence for the effectiveness of IPE is mixed, Barr et al. (2005) have proposed a useful framework for evaluating six levels of IPE outcomes. The Building a Better Tomorrow Initiative (BBTI) was a continuing professional development (CPD) program established to enhance the collaborative competencies of primary health care providers and foster interprofessional collaboration in primary health care settings. This article describes the evaluation design, specific measures, and educational outcomes of the BBTI program using Barr and colleagues' evaluation framework. METHODS: We used a mixed method approach. The evaluation research design was a one-group, pre- to poststudy utilizing a combination of quantitative and qualitative evaluation instruments and methods to collect data for the six levels of the Barr et al. evaluation framework. Evaluation focused especially on the following levels: participant satisfaction (reaction), confidence change (attitudes), performance change, and organizational impact. RESULTS: Participants were very satisfied with the BBTI modules and reported significant increases in their confidence and interprofessional collaborative competencies upon return to their primary health care practice sites. Interviews and focus groups with participants and administrators suggest that the BBTI modules were also effective in promoting and fostering interprofessional collaboration within primary health care settings. DISCUSSION: Results suggest that interprofessional CPD is effective in enhancing understanding of the roles of other professions, fostering respect and positive attitudes toward interprofessional collaboration, developing collaborative competencies, and promoting organizational change.
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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 | 0.043 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".