Exploring emerging physician competencies: Analyzing insights from medical care influencers on X
Bibliographic record
Abstract
PURPOSE: In the post-COVID era, recognizing evolving physician competencies is crucial for guiding medical education and test development. This study aimed to extract valuable insights concerning emerging physician competencies from influencers' posts on X, leveraging an AI-driven approach. METHOD: Two datasets pertaining to medical competency were analyzed, with posts collected from January 1, 2020, to June 1, 2023. Social network analyses were performed to identify influencers leading medical competency conversations on X. ChatGPT was utilized for textual analyses of influencers' posts to reveal core themes of physician competencies. RESULTS: Social network analysis revealed that medical professionals played a predominant role in disseminating information on medical competency on X. Textual analysis identified six core themes in the CanMEDS dataset-clinical learning environment, anti-racism, EDI, adaptive expertise, planetary health, and leadership development-and seven in the MedEd dataset-cultural competency, structural competency, assessment models, virtual care, EDI, leadership development, and wellness. CONCLUSION: The identified themes emphasize physicians' competencies in addressing health disparities, preparing for real-world challenges, adapting to the evolving healthcare landscape, and leading effectively in diverse healthcare settings. The findings hold significant implications for medical education, test development, and the integration of artificial intelligence in physician competency assessment.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".