Developing a team-based assessment strategy: direct observation of interprofessional team performance in an ambulatory teaching practice
Bibliographic record
Abstract
<ns4:p> <ns4:bold>Background:</ns4:bold> High functioning interprofessional teams may benefit from understanding how well (or not so well) a team is functioning and how teamwork can be improved. A team-based assessment can provide team insight into performance and areas for improvement. Though individual assessment via direct observation is common, few residency programs in the United States have implemented strategies for interprofessional team (IPT) assessments. </ns4:p> <ns4:p> <ns4:bold>Methods:</ns4:bold> We piloted a program evaluation via direct observation for a team-based assessment of an IPT within one Internal Medicine residency program. Our teams included learners from medicine, pharmacy, physician assistant and psychology graduate programs. To assess team performance in a systematic manner, we used a Modified McMaster-Ottawa tool to observe three types of IPT encounters: huddles, patient interactions and precepting discussions with faculty. The tool allowed us to capture team behaviors across various competencies: roles/responsibilities, communication with patient/family, and conflict resolution. We adapted the tool to include qualitative data for field notes by trained observers that added context to our ratings. </ns4:p> <ns4:p> <ns4:bold>Results:</ns4:bold> We observed 222 encounters over four months. Our results support that the team performed well in measures that have been iteratively and intentionally enhanced – role clarification and conflict resolution. However, we observed a lack of consistent incorporation of patient-family preferences into IPT discussions. Our qualitative results show that team collaboration is fostered when we look for opportunities to engage interprofessional learners. </ns4:p> <ns4:p> <ns4:bold>Conclusions:</ns4:bold> Our observations clarify the behaviors and processes that other IPTs can apply to improve collaboration and education. As a pilot, this study helps to inform training programs of the need to develop measures for, not just individual assessment, but also IPT assessment. </ns4:p>
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".