Postpartum depression: the (in) experience of <scp>B</scp>razilian primary healthcare professionals
Bibliographic record
Abstract
AIM: This article reports experiences of Brazilian physicians and nurses caring for women with postpartum depression in primary healthcare settings. BACKGROUND: Prevalence of postpartum depression in Brazil ranges from 12-37%, which fits with international claims about differences in the magnitude of the problem and consistency of screening. DESIGN: Qualitative descriptive. METHOD: This study was situated in health units of the Family Health Strategy in Campina Grande, Brazil. Data were collected from September 2010-January 2011 through open-ended interviews with ten nurses and seven physicians, observations, and field diary records. Inductive content analysis was used to develop categories. FINDINGS: Three categories: (1) Limited professional exposure to postpartum depression; (2) Postpartum depression as the domain of psychiatry; and (3) Challenges dealing with postpartum depression demonstrated that few professionals felt postpartum depression merited their attention. Women, with signs of postpartum depression, were usually identified by family members who noticed behaviours that seemed abnormal. Care providers indicated they had inadequate time and access to screening techniques to identify women with depression attending unit-based pregnancy and postpartum groups. When identified, women were referred directly to psychiatric care. CONCLUSION: Without consistent screening and diagnostic techniques, Brazilian health professionals are insecure about identifying and treating cases of postpartum depression. Referring women to psychiatric units entail more time for women to be diagnosed and treated and increased costs for health services. Primary healthcare professionals require training to screen, identify, and treat postpartum depression in primary healthcare settings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".