Western University (No. 10 Canadian Stationary Hospital and No. 14 Canadian General Hospital): a study of medical volunteerism in the First World War
Bibliographic record
Abstract
SUMMARY: The Canadian government depended on chaotic civilian volunteerism to staff a huge medical commitment during the First World War. Offers from Canadian universities to raise, staff and equip hospitals for deployment, initially rejected, were incrementally accepted as casualties mounted. When its offer was accepted in 1916, Western University Hospital quickly adopted military decorum and equipped itself using Canadian Red Cross Commission guidelines. Staff of the No. 10 Canadian Stationary Hospital and the No. 14 Canadian General Hospital retained excellent morale throughout the war despite heavy medical demand, poor conditions, aerial bombardment and external medical politics. The overwhelming majority of volunteers were Canadian-born and educated. The story of the hospital's commanding officer, Edwin Seaborn, is examined to understand the background upon which the urge to volunteer in the First World War was based. Although many Western volunteers came from British stock, they promoted Canadian independence. A classical education and a broad range of interests outside of medicine, including biology, history and native Canadian culture, were features that Seaborn shared with other leaders in Canadian medicine, such as William Osler, who also volunteered quickly in the First World War.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.035 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".