The training of obstetric physicians and nurses to change the obstetric model in Brazil: A view of the preceptors in the training process
Bibliographic record
Abstract
Background and objective: In obstetrics training, there are gaps in the scientific evidence on how to teach safe practices with respect. The objective of this study was to explore from the point of view of the preceptors how the process of training obstetricians (physicians and nurses) in residency leads to the development and inculcation of the practices recommended by the national and international guidelines for assistance with natural childbirth.Methods: Qualitative, exploratory-descriptive study. Thirty-five professionals, including 21 physicians and 14 nurses, from a public institution in the Midwest of Brazil participated in the study. Data were collected through face-to-face interviews conducted from March to June 2018. They were categorized into emerging themes, supported by NVivo to natural birth ® software. Two researchers reviewed the data, and by consensus, the identified issues were confirmed.Results: Of the participants’ comments, 4 themes were codified: approach of the good practices in natural childbirth care; unnecessary practices that remain in use; norms and routines in natural childbirth care; and work processes in the obstetric residency program.Conclusions: The results highlight the necessity of reorganization of the work processes in the residency program, with continuous action directed toward the strengthening of pedagogical processes and the qualification of the actors involved in the formation and organization of childbirth care services to expand the disruptive potential of new health professionals.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".