Leading Educationally Effective Family-Centered Bedside Rounds
Bibliographic record
Abstract
BACKGROUND: Family-centered bedside rounds (family-centered rounds) enable learning and clinical care to occur simultaneously and offer benefits to patients, health care providers, and multiple levels of learners. OBJECTIVE: We used a qualitative approach to understand the dimensions of successful (ie, educationally positive) family-centered rounds from the perspective of attending physicians and residents. METHODS: We studied rounds in a tertiary academic hospital affiliated with the University of Calgary. Data were collected from 7 focus groups of pediatrics residents and attendings and were analyzed using grounded theory. RESULTS: Attending pediatricians and residents described rounds along a spectrum from successful and highly educational to unsuccessful and of low educational value. Perceptions of residents and attendings were influenced by how well the environment, educational priorities, and competing priorities were managed. Effectiveness of the manager was the core variable for successful rounds led by persons who could develop predictable rounds and minimize learner vulnerability. CONCLUSIONS: Success of family-centered rounds in teaching settings depended on making the education and patient care aims of rounds explicit to residents and attending faculty. The role of the manager in leading rounds also needs to be made explicit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".