Saving the Endangered Physician-Scientists: Reintroducing Them to An Environment of Administrative Support
Bibliographic record
Abstract
On March 11, 2020, the alarming spread and severity of the COVID-19 disease led the WHO to declare it as a pandemic. Around the globe, entire cities and nations came to a grinding halt. While many industry sectors projected and prepared for a steady decline in business, other areas boomed as demand increased at an unprecedented rate. Indubitably, and as is befitting during a pandemic, the healthcare industry revved up to deliver its best. Specifically within healthcare, administrators at all levels worked to establish the new normal for delivering care in a virtual world. Within this niche, administrative assistants for physician-scientists worked in overdrive. Calendars needed to be reorganized, conferences needed to be postponed, research teams and materials needed to be moved to online interfaces, and in-person meetings needed to be rescheduled to the videoconference platform of choice. The goal remained the same: to support the physician-scientists in all capacities so that they could continue to be their best version of clinicians and researchers. In many ways, this pandemic has highlighted the importance of effective administrative support to the functionality of the physician-scientist. Perhaps even giving some new insight into the prevailing issue of ‘saving’, what Jain et al have dubbed as, ‘the endangered physician-scientist’.1 Physician-scientists play an integral role in the medical community, they are often the protagonists that drive forward the narrative of medical discovery and novel therapies. However, in the last few decades, the evolving role of the physician-scientist and the overwhelming demands of the job have led to a steady decrease in the number of …
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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 | 0.025 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.021 | 0.025 |
| Scholarly communication | 0.029 | 0.038 |
| Open science | 0.005 | 0.033 |
| Research integrity | 0.016 | 0.044 |
| Insufficient payload (model declined to judge) | 0.014 | 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".