To develop the "habit" : Nurses and prenatal care for poor women in the United States and Great Britain, c. 1880-1939
Bibliographic record
Abstract
Barbara Keddy, Professor Emerita, School of Nursing, Dalhousie University presents the History of CAHN/ACHN and Janet Greenlees, Senior Lecturer Social Sciences, Media & Journalism (history); Senior Director Centre for the Social History of Health & Healthcare, Glasgow, Caledonian University delivers the Hannah Lecture sponsored by Associated Medical Services at the 2018 Canadian Association for the History of Nursing Annual Conference. Prenatal care provides an opportunity to improve mothers’ general health during pregnancy and to identify high-risk mothers for specialist care. By the 1920s, it became widely accepted policy in Western countries including Britain and the United States that all pregnant women should receive medical checks. However, poor women were then, and still are now, much less likely than their wealthier counterparts to engage with preventive healthcare. This paper examines the role nurses and midwives played in introducing municipal and voluntary prenatal provision in areas of socio-economic deprivation and in securing uptake in three cities - Philadelphia, Liverpool and Glasgow. All three port cities had high immigration levels and significant pockets of poverty. This paper highlights the role of the coordinating services and local nurse initiatives. While local variations in the process and politics of practice are evident, using a combination of district nursing and municipal records, this paper reveals how some commonalities in caring for poor, pregnant women transcended region. The boundaries of responsibility surrounding health and social welfare, either self-defined or professionally set established parameters around pregnancy care and could help or hinder securing women’s engagement with prenatal care. Both the similarities and differences between the communities suggest the need for more international studies of relationships between prenatal healthcare and social welfare if current provision is to better address the needs of all its constituents. While over the past 100 years maternity care in the USA, England and Scotland has undergone many scientific, technological and locational changes, the relationship between prenatal care and social welfare remains contentious in each country.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".