2022 Nursing History Symposium
Bibliographic record
Abstract
Pandemic Caring: public health nursing and community in the history of infectious disease The 1918-19 influenza pandemic demonstrated the power of nursing in a disease crisis. At the time, and later in the eyes of historians, nursing interventions were valued because they alleviated suffering and meant an increased chance of survival when there were few medical treatment options. Much of this nursing care was delivered outside formal hospital settings, in locales that blurred the boundaries between institution, community, and home. In local neighbourhoods, public health nursing and private nursing organizations had for decades served those with virtually no access to health care, in places where infectious disease was a constant risk and a leading cause of mortality and disability. This form of nursing – in homes, at mission houses, for private agencies such as the VON – played a role historically that we barely recognize today, when the face of pandemic nursing is critical care. Historical resonances nonetheless abound. Public health leaders are now calling for a return to community and neighbourhood-level engagement and healthcare investment, partly in response to pandemic inequality and vaccine access. This paper will draw from historical analyses of community-level nursing in the past and suggest ways in which nursing might engage with those successes and failures. With Dr. Esyllt Jones, University of Manitoba, who delivered the keynote address.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.265 | 0.097 |
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".