Barreras en mujeres indígenas para acceder a servicios obstétricos en el marco de redes integradas de servicios de salud
Bibliographic record
Abstract
Analizar las barreras que enfrentan las mujeres indígenas para acceder a la red de servicios obstétricos en el marco de Redes Integradas de Servicios de Salud. Se diseñó un estudio transversal descriptivo que integró métodos cuantitativos y cualitativos. La muestra fue intencionada, no probabilística. La recolección de datos se hizo en Oaxaca, México, durante 2017 y 2018. Se encuestó a 149 mujeres indígenas usuarias de servicios obstétricos para caracterizarlas sociodemográficamente y se seleccionaron 30 que tuvieron complicaciones durante el proceso de embarazo y parto para realizarles una entrevista semiestructurada. Se realizó observación no participante. La red de servicios obstétricos agrupa la atención de cuatro instituciones con diferentes modelos de atención, y por ello diversos tipos de establecimientos y recursos humanos para atender a las mujeres indígenas. Casi el 20% de las mujeres no iniciaron control prenatal en el primer trimestre del embarazo y el 27,2% tuvieron complicaciones durante el periodo gestacional. Las principales barreras fueron de disponibilidad (horarios de atención, aspectos geográficos), accesibilidad (carencia de recursos financieros), aceptabilidad (prácticas ancestrales vs. recomendaciones médicas) y continuidad del servicio (dificultades para ingresar al segundo nivel de atención y a especialidades médicas). El modelo de atención en redes posibilita el acceso a los servicios obstétricos, pero no garantiza la atención. Para ello, es necesario mejorar tanto la infraestructura de las instituciones prestadoras de servicios obstétricos como los procesos de atención. Hay que ampliar la visión del modelo considerando la perspectiva de derechos humanos y de equidad en salud. To analyze the barriers that indigenous women face in access to the network of obstetric services in the context of the implementation of integrated healthcare networks (IHN). We designed a cross-sectional descriptive study including quantitative and qualitative methods. Sampling was intentional, no probabilistic. Data collection was carried out in Oaxaca, Mexico, during 2017-2018. A total of 149 indigenous women who used obstetrical services were surveyed and sociodemographic characteristics were obtained. Later were selected 30 cases that had complications during pregnancy and childbirth for a semi-structured interview. Non-participant observation was conducted. The network of obstetric services comprises four institutions with different models of care and therefore different types of facilities and human resources to assist indigenous women. Nearly 20% of women did not start prenatal care in the first trimester of pregnancy and 27.2% had complications during the gestational period. The main barriers were availability (hours of operation, geographical aspects), accessibility (lack of financial resources), acceptability (ancestral practices vs. medical recommendations), and continuity of service (difficulties for admit patients in hospitals referred from first line of care). The networks model allows access to obstetric services but does not guarantee care. For this it is necessary to improve both: the infrastructure of the obstetric service providers, and the care processes. It is necessary to broaden the vision of the IHN management model considering the perspective of human rights and equity in health.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".