Video Experimental Relacionado
Updated: Jun 25, 2026
14:43
A Novel Method for Involving Women of Color at High Risk for Preterm Birth in Research Priority Setting
Published on: January 12, 2018
Efecto de una intervención participativa con grupos de mujeres en los resultados de los nacimientos en Nepal: ensayo
Dharma S Manandhar1, David Osrin, Bhim Prasad Shrestha
1Mother and Infant Research Activities (MIRA), PO Box 921, Kathmandu, Nepal.
Lancet (London, England)
|September 15, 2004
Resumen
Una intervención participativa basada en la comunidad redujo significativamente las tasas de mortalidad neonatal en Nepal al empoderar a los grupos de mujeres para abordar los desafíos perinatales locales. Este enfoque de bajo costo mejoró los resultados del parto y la salud materna en una población vulnerable.
Área de la Ciencia:
- Salud Global La salud global.
- Salud Materna y Infantil.
- Intervenciones basadas en la comunidad.
Sus antecedentes:
- Las muertes neonatales contribuyen significativamente a la mortalidad de menores de 5 años en los países en desarrollo.
- Los partos en el hogar son frecuentes entre las poblaciones más pobres, lo que aumenta los riesgos neonatales.
- Se están explorando enfoques participativos basados en la comunidad para reducir la mortalidad neonatal.
Objetivo del estudio:
- Evaluar la efectividad de una intervención participativa basada en la comunidad para reducir las tasas de mortalidad neonatal.
- Evaluar el impacto de los grupos de mujeres en los problemas perinatales y los resultados de salud.
- Determinar la escalabilidad y sostenibilidad de la intervención.
Principales métodos:
- Se realizó un ensayo controlado aleatorizado de grupo emparejado en pares en el distrito de Makwanpur, Nepal.
- Los grupos de mujeres, facilitados por mujeres locales, participaron en ciclos de acción-aprendizaje para identificar y abordar problemas perinatales.
- Los resultados del parto fueron monitoreados en una cohorte de 28.931 mujeres, con análisis por intención de tratar.
Principales resultados:
- La tasa de mortalidad neonatal fue significativamente menor en los grupos de intervención (26,2/1000) en comparación con los grupos de control (36,9/1000).
- La tasa de mortalidad materna se redujo sustancialmente en el grupo de intervención (69/100,000) frente a los controles (341/100,000).
- Las participantes de la intervención mostraron una mayor utilización de la atención prenatal, el parto institucional y las prácticas higiénicas de nacimiento.
Conclusiones:
- Una intervención participativa de bajo costo utilizando grupos de mujeres puede mejorar significativamente los resultados de los nacimientos en entornos con recursos limitados.
- La intervención demostró potencial de sostenibilidad y escalabilidad en la mejora de la salud materna y neonatal.
- Empoderar a las comunidades locales a través de enfoques participativos es efectivo para abordar desafíos críticos de salud.
Videos de Conceptos Relacionados
02:01Group Design
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to...
01:52Regression Toward the Mean
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
01:24Testing a Claim about Population Proportion
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
01:11Testing a Claim about Mean: Known Population SD
A complete procedure of testing the hypothesis about a population mean is explained here.
Estimating a population mean requires the samples to be distributed normally. The data should be collected from the randomly selected samples having no sampling bias. The sample size needed to be higher than 30, and most importantly, the population standard deviation should be already known.
In most realistic situations, the population standard deviation is often unknown, but in rare circumstances, when it...
Estimating a population mean requires the samples to be distributed normally. The data should be collected from the randomly selected samples having no sampling bias. The sample size needed to be higher than 30, and most importantly, the population standard deviation should be already known.
In most realistic situations, the population standard deviation is often unknown, but in rare circumstances, when it...
01:13Randomized Experiments
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Simple randomization
Simple...