COVID-19 and the Birth of the Virtual Doula
Bibliographic record
Abstract
As doulas and anthropologists, we have anxiously followed North American hospital policies that limit the number of support people who may accompany a birthing person to the hospital. We’ve wondered what these shifting policies mean for doulas – birth workers who provide continuous emotional, physical, and informational support to pregnant and laboring people – and those they accompany during labor. The COVID-19 pandemic comes on the heels of increasing attention to a maternal health crisis that disproportionately impacts people of color and the poor. Doulas, long seen as serving upper middle class white women, are increasingly becoming a valuable source of advocacy in the face of birth disparities. In data collected from 450 qualitative surveys, we address how US and Canadian doulas adapted to the constraints posed by virtual support, how this then shifted their perceptions of care, and how virtual doula work exposes existing inequalities.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.033 | 0.033 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 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".