Better care and better teaching. New model of postpartum care for early discharge programs.
Bibliographic record
Abstract
PROBLEM BEING ADDRESSED: Rapid postpartum discharge has reduced opportunities to detect early newborn or parenting problems and to teach neonatal assessment and maternal postpartum care to medical trainees. OBJECTIVE OF PROGRAM: Development of a program to not only ensure adequate care of mothers and newborns after early hospital discharge, but also to teach outpatient assessment skills to family medicine residents. MAIN COMPONENTS OF PROGRAM: In an urban, secondary care, university-affiliated teaching hospital predominantly training family medicine residents, an interdisciplinary committee created and supervised a neonatal and maternal postpartum assessment program. Newborn infants and their mothers are seen by a family physician, a family medicine resident, and a nurse within 48 hours of discharge, after which care is assumed in the community by the child's primary care physician. An assessment protocol developed by the interdisciplinary group promotes standardized mother and child care and a structured learning experience for trainees. CONCLUSION: Rapid follow up of early discharged infants and their mothers can be facilitated by a program of standardized assessment by a roster of pooled, interacting family physicians and nurses. When this assessment occurs in a teaching milieu, a comprehensive learning experience can be combined with defined objectives that emphasize and encourage newborn and maternal assessment for ambulatory patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".