Práticas sociais de medicalização & humanização no cuidado de mulheres na gestação
Bibliographic record
Abstract
The study's main objective is to analyze how discourses of medicalization and humanization reconnect in primary healthcare and shape prenatal care for pregnant women provided by family health teams. This was a single and integrated case study with multiple analytical units and a qualitative approach. A total of 17 focus groups were performed, in which 47 health professionals were heard (14 physicians, 19 nurses, and 14 dentists), members of 17 family health teams in 16 municipalities in the South of Brazil. The empirical material was analyzed from the perspective of Foucauldian discourse analysis. The family health teams, adopting general practice, reported difficulties in conducting prenatal care, evoking and bolstering the discourse of obstetric medicalization that their practice should supposedly offset. The discourse officially adopted by humanization, prioritized in the generalist model of prenatal care, continues to function as a complementary discourse to that of medicalization and specialization, which prevails in the practices reported by the teams. The emphasis on humanized care for pregnant women tests the limits of professional territories and assumes the renegotiation of competencies. Efforts at collaboration between the family health teams and obstetricians have not proved very successful in this specific case.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".