Mothering Work and Perinatal Transfer: An Institutional Ethnographic Investigation
Bibliographic record
Abstract
BACKGROUND: While maternal or infant transfer is generally the safest course of action when health complications arise, the process of shifting from one hospital to another is stressful for mothers and their infants. There is limited understanding of how institutional processes coordinate patient transfer in ways that increase tensions for women and their families who are trying to navigate the institutional systems during health crises. METHODS: This institutional ethnographic study explored womens' experience of transfer. Interviews were conducted with a purposive sample of six childbearing women. The analysis highlights tensions and contradictions between patient care and institutional demands and shows how ordinary institutional decision-making practices impacted participants in unexpected ways. RESULTS: Women experienced uncertainty and stress when trying to convince health-care providers they needed care. Before, during, and after transfer, participants navigated home responsibilities, childcare, and getting care closer to home in difficult circumstances. CONCLUSION: The effort and skill women need to care for their infants and families as they are transferred is extraordinary. This study offers insight into the resources and support childbearing women need to accomplish the work of caring for their families in the face of perinatal crisis and multiple transfers.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".