Development of a wellness module for PGY-1 academic half-day
Bibliographic record
Abstract
Poster (3E) Purpose:Wellness is an increasingly important component of residency program curriculum.Physicians should develop resiliency skills to maintain wellness, to avoid burnout, and to provide safe patient care.However, finding opportunities to build wellness resiliency in residency training can be challenging.In addition, it is unclear whether or not residents are aware of services that are currently available.This study involved a comprehensive needs assessment among all residents in the Faculty of Medicine, Memorial University of Newfoundland (MUN).Information was utilized to inform academic half-day (AHD) content for PGY-1 trainees. Methods:A survey was developed based on a literature review and local wellness services.The survey was distributed electronically or in person to residents in all training programs (n=287) at MUN.The survey assessed the residents' knowledge of current wellness services.Results from survey were analyzed and utilized to inform educational content.An AHD module was subsequently designed to include these concerns.Case examples were formulated to be used for discussion.Post AHD evaluation was provided to examine residents' satisfaction and feedback for future sessions. Results:126/287 residents completed the survey, yielding a response rate of 44%.Approximately 54% (n=60/112) of residents reported being only slightly knowledgeable about resident wellness.70% (n=77/110) of all respondents indicated they were not aware that services were confidential.Qualitative feedback themes included mandatory activities in each discipline and the need for wellness role models.Residents provided suggestions for wellness promotion including social media, consistent integration into AHDs, and options for rural/remote residents.Ideas for AHD content included mindfulness, difficult conversations, dealing with burnout, and resiliency.The needs assessment data was subsequently used to develop educational content for an AHD.PGY-1 residents participated in the AHD (n=38).Post session evaluation surveys were collected.Session was divided into two groups and included case based discussion on topics identified in the needs assessment. Conclusion:Wellness is an important theme in postgraduate medical education.The findings from this preliminary survey suggest that a significant proportion of MUN residents are unware of current wellness resources.Our AHD demonstrated that a larger group didactic session was a less effective format for introducing residents to wellness competencies.Further work is needed to enhance wellness opportunities and competencies in our postgraduate training programs.
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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 | 0.007 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 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".