Bibliographic record
Abstract
In Reply to Walaszek and Reardon: In light of the recent debate around the Affordable Care Act, the authors’ reminder that medical specialties must partner more effectively with primary care providers, and their corresponding call for training residents in advocacy as necessary to translate evidence-based practice into changes that will improve the mental health system, could not have come at a more crucial time. We know that the prevalence of illness follows a social gradient.1 So, why do specialists continue to focus primarily on clinical interventions, only to then send patients back to the conditions that made them sick? In our ongoing study of social accountability in medicine and the physician’s role as health advocate, we find that some residency program directors, representing a range of specialties, are struggling to balance the lure of genomic medicine (despite the accumulating evidence of its limitations) with the fundamentals of population medicine—a crucial issue for the future of medical education.2 We observe that many residents in different specialties are seeking good role models to emulate, as well as opportunities to collaborate on health advocacy initiatives with trainees from other medical specialties, particularly from primary care. Trainees point out that if health advocacy and partnerships with primary care providers are going to be taken seriously, trainees need to experience these in medical training and witness them in medical practice. Yet, this proposition continues to appear daunting in light of a predominantly siloed, clinical, and subspecialty-oriented medical training structure, without a reasonable effort to integrate the “incontrovertibly social nature of disease.”3 While institutions outside of the core medical system call for a more sensitive, compassionate, and community-responsive physician, the existing medical environment rewards the opposite. The current system does not enable specialists to practice in an intraprofessional manner, nor does it emphasize particular attention to the multiple determinants of health outcomes. Training residents for advocacy and to collaborate more decisively with primary care providers will require medical schools to establish principles of social accountability, drawing on an explicit, three-tier engagement: (1) identifying current and prospective societal priorities, (2) adapting medical training to equitably meet the priorities, and (3) verifying that anticipated effects have benefited society.4 Shafik Dharamsi, MSc, PhD Associate professor, Department of Family Practice, and faculty lead of social accountability and community engagement, Faculty of Medicine, University of British Columbia, Vancouver, Canada; [email protected] Robert Wollard, MD Professor, Department of Family Practice, Faculty of Medicine, University of British Columbia, Vancouver, Canada.
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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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.009 | 0.029 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.034 | 0.070 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".