Observations and reports of incidents of how birthing persons are treated during childbirth in two public facilities in Argentina
Bibliographic record
Abstract
OBJECTIVE: This study sought to estimate the frequency and types of mistreatment during childbirth and explore health professionals' opinions on barriers/facilitators to providing respectful childbirth care. METHODS: This prospective mixed-methods investigation consisted of direct observations of childbirth (n = 250), at-home surveys with birthing individuals (n = 45), and qualitative in-depth health staff interviews (n = 6), conducted between January and July 2019, in two public facilities in Argentina. Frequencies of clinical practices and mistreatment and 5% confidence intervals were calculated. A logistic regression analysis was also conducted to examine associations between mistreatment and covariates of interest, with P < 0.05 considered statistically significant. RESULTS: Overall, 61/250 (24.4%, confidence interval 19.6%-30.6%) observations recorded instances of mistreatment; 20/45 surveyed participants (44.4%) reported at least one episode of mistreatment. The most frequent perpetrators were physicians (35.6%), birth companions (24.4%), midwives (22.2%), and nurses (13.3%). Participants with lower educational attainment and those racialized as non-white had higher odds of being mistreated. Health providers reported that respectful childbirth is currently widely implemented due to authorities' and communities' awareness on respectful birth´s rights. CONCLUSION: Almost a quarter of birthing people were observed to suffer mistreatment - primarily verbal abuse - and 44.4% of surveyed individuals reported mistreatment. Future research is needed to determine how to ensure the provision of respectful childbirth care for all. A quarter of participants experienced mistreatment; mostly those with lower educational attainment and/or racialized as non-white. Further research on implementation of respectful childbirth is warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".