Bibliographic record
Abstract
Journal of Nurse-Midwifery (the previous name of the Journal of Midwifery & Women's Health).In her 1981 editorial, Carrington calls for recruiting individuals of color into the midwifery profession.When I read her words, I was stunned and saddened by how many of them could have been written today.Carrington's editorial resonated so strongly with me that I want to share words from her editorial and invite the Journal's readers to think about her message in light of current events in midwifery.Carrington begins by citing racial and ethnicity data from a 1976 to 1977 survey of nurse-midwives.Among the 1248 ACNM members who participated in the survey, 89.6% identified as European, 5.9% as African, 2.2% as Asian, 1.4% as Hispanic, and 0.6% as American Indian (terms reflect those used in the editorial).The most recently published ACNM membership survey from 2012 had an even higher proportion (91.6%) of white midwives.2 Carrington notes that the proportion of midwives of color is smaller than the proportion of people of color in the United States.This fact remains true today, as the United States is well on its way to a becoming a majority-minority nation in which less than 50% of the nation's total population will be non-Hispanic white individuals.3 Carrington states:This very low representation of ethnic minorities in American nurse-midwifery must concern us all as it is not only incredible but is totally unsatisfactory.The following are my reasons for making this statement:
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.037 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.022 |
| Scholarly communication | 0.021 | 0.024 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.016 | 0.029 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".