Almonte’s Great Train Disaster: Shaping Nurses’ Roles and the Civilian Use of Blood Transfusion
Bibliographic record
Abstract
Blood transfusion was initially a small-scale, labour-intensive therapy administered by physicians. Through the first decades of the 20th century, transfusion comprised a "last resort" measure used and tested primarily in the context of war. Media accounts of the Almonte train disaster on the night of 27 December 1942 linked survival to the newly established blood bank located 42 km east in Ottawa, Ontario. This event did not constitute a "first time" occurrence or a "great discovery" in the history of blood. But it did illustrate in a very visible and public manner that blood transfusion technology was now readily available for use in general hospitals and civilian populations. Canada had an infrastructure for the collection, processing, storage, and transportation of blood products, and for the recruitment of blood donors by the mid-1940s. As the need for blood declined toward the end of World War II, transfusion became a technology in need of application. The extension of transfusion to civilian populations, however, would require a ready source of labour-increased numbers of health care workers who were available continuously with the necessary knowledge and skills to assume the responsibility. Nurses were well situated for this technological role by a convergence of scientific, economic, labour, gender, professional, and educational influences that both facilitated and constrained blood transfusion as a nursing competency. This paper examines how the expanded use of one medical technology shaped related roles for nurses. Transfusion ultimately influenced nurses' work and the composition of the workforce as the first medical act "delegated" to nurses in Ontario (1947), setting a precedent for the delegation of further technologies over the next four decades.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.025 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".