Managing Disruptive Physician Behavior: First Steps for Designing an Effective Online Resource
Bibliographic record
Abstract
Interviews with physician leaders from hospitals in a mid-sized Ontario City were conducted to determine their needs with regard to managing disruptive physician behaviour. These findings were used to inform the design of a two-day skill-development workshop for physician leaders on disruptive behaviour. The workshop was evaluated using a modified version of the Learner Experience Feedback Form, which was built to align with W(e)Learn, http://www.ennovativesolution.com/WeLearn/ a framework developed to guide the design, delivery, development, and evaluation of online interprofessional courses and programs (MacDonald, Stodel, Thompson, & Casimiro, 2009). The surveys gathered information related to the content, media, service, structure, and outcomes of the workshop. The findings from the focus group interviews and workshop evaluation identify physician leaders’ needs with regard to disruptive behavior and were used to inform the design of the world’s first Online Physician Health and Wellness Resource http://www.ephysicianhealth.com/ an open access learning resources currently being used globally, in 91 countries. The resource was the recipient of the winner of the International Business/Professional 2010 International eLearning Award. The findings demonstrated the importance of conducting a needs analysis and using a framework to guide the design, delivery and evaluation of effective online healthcare education.
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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 | 0.024 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".