146: Leading educationally effective family centered rounds: it takes a good manager!
Bibliographic record
Abstract
Family centered rounds (FCR) are the most common rounding format in North American pediatric hospitals. If conducted with a clear understanding of the settings, goals, and teaching strategies, FCR permit explicit teaching of CanMEDS competencies. If not done effectively, learners and teachers run the risk of being led room to room with suboptimal participation, minimal educational value and very poor efficiency. The purpose of this study was to describe the dimensions of FCR within the Canadian CanMEDS context in order to develop recommendations for effective teaching and learning earning in thi this e environment. Data were collected from focus group meetings of pediatric residents and attending paediatricians on the clinical teaching unit at a tertiary care academic children's hospital. Data were analyzed using grounded theory methodology. Rounds were described along a spectrum from successful and highly educational to unsuccessful and of low educational value. Three dimensions impacted the rounds experience; the environment, education, and competing priorities. The physical aspects of the hospital learning environment and the fact that the environment was both family centred and team based impacted educational success. Both the content and the process (or how teaching was done) affected the experience. Last, preceptors and senior residents faced competing priorities between patient care, learners (and the range of learners) and the need to maximize efficiency and patient numbers. Attending paediatrician and resident perceptions of educational effectiveness were influenced by how well these dimensions were managed. The ability to manage the rounds was the core variable for successful rounds being led by individuals who were able to maximize predictability in the environment and minimize learner vulnerability. Specific strategies for leading educationally effective FCR were also discussed. The CanMEDS Manager Role was central to creating an educationally effective FCR experience and this is an ideal environment for the teaching and evaluation of this core competency. A framework and tool for leading successful FCR based on the CanMEDS Manager role was developed based on study findings. The importance of the Manager Role needs to be made explicit for trainees and attending staff.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".