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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".