Indigenous factors relevant for Safe Birth in Cultural Safety among Nancue ñomndaa communities in Guerrero, Mexico
Bibliographic record
Abstract
Culturally unsafe approaches have governed the study of Indigenous birthing systems in the South of Mexico. The actions that these approaches promote tend to perpetuate the dominance of Western views in the shaping of health care systems; thus, reducing their cultural pertinence and quality. In this protocol, we propose a methodology to understand the most relevant factors associated with safe birth according to the knowledge of traditional Indigenous midwives. We propose to use conversations as a methodology to promote intercultural dialogue. Conversations recognize mutual interaction and construction of meaning, thus allowing for Western and Indigenous practitioners to interchange knowledge and mutually enrich each other. Three experienced traditional midwives will participate in one-to-one conversations with an indigenous researcher. They will provide the first level of understanding on the meaning of relevant factors for safe birth in their communities. A group of non-indigenous Academic researchers will participate in the process sharing their knowledge about the issue and support the analysis process. These initial results will go to a group session with traditional midwives and their apprentices to check the content, suggest additional elements and share the knowledge among them. This study is part of a bigger effort to support and strength the practices of the traditional midwives in these communities.
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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 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".