Osler’s bedside library: Updated essentials from medical students
Bibliographic record
Abstract
Over 100 years ago Sir William Osler made a list of ten readings that should be at the bedside of every medical student reflecting Osler’s broad and prolific reading habits. Time has continued to flow since Osler’s reading list was proposed and thus, its place in current day medical education warrants reflection. Fundamental questions in this respect include: do medical students still read in the same capacity as their century-old counterparts? If so, how widely does Osler’s original reading list apply to current medical students? Finally, what works would today’s medical students recommend having at the bedside? Students representing the classes of 2016 to 2020 were surveyed at Dalhousie Medical School, constituting one of the largest student efforts in the literature to date to provide per- spectives on Osler’s bedside reading list as well as consensus towards an updated library.This study highlights the considerable disconnect between Osler’s original bedside list and reading tendencies of current medical students. Dalhousie medical students predominantly read and are influenced largely by works pertaining to the medical field and physicians, as opposed to the broad selections set forth by Osler.Although this may reflect the changing medical field or the immersive nature of current training, it also urges reflection as to the value Osler saw in his original bedside library and whether these lessons may be lost on future generations of physicians.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".