Training Residents in Maternal Depression Care to Improve Child Health: A CERA Study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Untreated maternal depression negatively impacts both the mother and her children's health and development. We sought to assess family medicine program directors' (PDs) knowledge and attitudes regarding maternal depression management as well as resident training and clinical experience with this disorder. METHODS: Data were gathered through the Council of Academic Family Medicine's (CAFM) Educational Research Alliance (CERA) national survey of family medicine PDs in US and Canadian programs, from January through February, 2018. RESULTS: Surveys were completed by 298 PDs (57.1% response rate) who were majority male (58.9%) and white (83.8%). Nearly all (90.2%) PDs agreed that family physicians should lead efforts to minimize the impact of maternal depression on child well-being. According to PD report, in the family medicine clinics where residents train, most (77.3%) have a clinic process that ensures that routine screening for depression occurs, and 54.4% do some screening of mothers during pediatric visits. Only 18.2% report routinely taking steps to minimize the impact of the mothers' depression on child well-being. Finally, 41.3% of PDs reported being familiar with the literature on the impact of maternal depression on children; self-reported familiarity was significantly associated with more comprehensive resident training on this topic. CONCLUSIONS: Family medicine residency program directors are supportive of training in maternal depression, though their current knowledge is variable and there are opportunities to enhance care of mothers and children impacted by this common and serious disorder.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".