Bibliometric and Textual Analysis of Historical Patterns in Maternal–Infant Health and Nursing Issues in <i>The Canadian Nurse</i> Journal, 1905–2015
Bibliographic record
Abstract
STUDY BACKGROUND: Journals are key learning mechanisms for nursing organizations. Analysis of publications provides opportunities to explore influences, priorities, and perspectives of nurses over time. PURPOSE: To identify historical trends in maternal-infant health and nursing practice. METHODS: Historical bibliometric and content analysis of articles in The Canadian Nurse, 1905-2015. Six hundred sixty-eight lead publications in the journal were identified. Data were extracted on authorship, writing style, geographical distribution, and language, and content themes were determined. RESULTS: Five hundred twenty-five publications were written by nurses, and 272 came from the Ontario and Quebec. Nine key content areas were identified, including changing families, women's bodies, prenatal care, birth care, postpartum care, when things go wrong, and keeping babies healthy. The number of maternal-infant publications in this national journal has been decreasing since the emergence of specialty journals. CONCLUSION: Advances in perinatal nursing practice over the past 115 years in Canada reflect emerging scientific developments and evolving social values. These articles traced the medicalization and reclamation of pregnancy and childbirth, the shifting role of nurses in relation to other health and social care providers, and the impact of determinants of health on the well-being of mothers, infants, and families.
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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 | 0.012 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.163 | 0.226 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".