Nursing in a time and place of Peril: Five heroic North Carolina nurses
Bibliographic record
Abstract
Nurses are all around us. They attend our births and deaths, administer healing treatments when we are ill and help us promote well-being through public health and mental health programs. Almost every family can identify a nurse or two on its family tree. Nurses are members of and care for members of every racial, religious and cultural group. For over a century, nurses have worked in rural and urban areas, provided care in chrome trimmed surgical suites and tumble down cabins and have navigated legal, political and economic currents to improve the health of the public while continuously upgrading the profession. While much nursing history has been chronicled by scholars, the record of North Carolina military nurses is virtually unknown. Illuminating the stories of a select group of nurses who have cared for soldiers from the Civil War through the current war on terror can offer insights and increase understanding of development of professional nursing and the evolving role of women in our society. Historical inquiry involves studying primary and secondary sources to increase our understanding of the past. Evidenced based source material may include written documents, oral histories, artifacts, photographs and new media such as websites and even “tweets”. Nurse historians use all of these forms of evidence to discover and analyze our collective professional heritage. Historical findings may be disseminated through oral, written, audio-visual and electronic means. The best method to report historical findings depends on the subject of inquiry. Prosopography, frequently referred to as collective biography, is a useful historical tool to chronicle a group of individuals with shared characteristics and/or experiences. While biographies and case studies focus on the uniqueness of a single person, prosopography allows the researcher to analyze the changing roles and status of a cluster of individuals. Using a prosopographic approach, this article analyzes the progress of professional nursing through the contributions of five North Carolina military nurses over the course of one hundred and fifty years.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.045 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".