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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
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Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
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Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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Statistical Process Control (SPC) is a method used to monitor and control quality within processes, particularly in manufacturing and service delivery, by employing statistical methods. SPC aims to distinguish between natural (common cause) variation and variation due to specific changes or events (special cause), allowing for timely improvements and sustained quality. The control chart, a pivotal tool in SPC, visually displays data over time alongside a central line of upper and lower control...
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In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
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Métodos de muestreo y consideraciones en la mejora de la calidad: una revisión de alcance

Lucylynn Lizarondo1, Vanesa Bochkezanian1, Alexa McArthur1

  • 1JBI, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, SA, Australia.

JBI evidence implementation
|December 24, 2025
PubMed
Resumen

El muestreo de mejora de la calidad (MC) prioriza métodos prácticos de no probabilidad para decisiones oportunas. Se necesita una guía más clara para un muestreo transparente y sensible al contexto en la atención médica.

Palabras clave:
PDSAevaluaciónciencia de la mejoramejora de la calidadtamaño de la muestramuestreo

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Área de la Ciencia:

  • Mejora de la Calidad de la Atención Médica
  • Ciencia de la Implementación
  • Metodología de Investigación

Sus antecedentes:

  • La mejora de la calidad (MC) utiliza métodos basados en datos para mejorar la atención médica.
  • Un muestreo eficaz es crucial para una recopilación de datos de MC factible y relevante.
  • Los métodos actuales de muestreo de MC son a menudo pragmáticos, se informan de manera inconsistente y carecen de justificación.

Objetivo del estudio:

  • Identificar y sintetizar estrategias de muestreo para iniciativas de mejora de la calidad (MC).
  • Examinar los marcos de muestreo y las consideraciones sobre el tamaño de la muestra en la MC.
  • Situar el muestreo de MC en el contexto más amplio de la implementación de evidencia y la ciencia de la implementación.

Principales métodos:

  • Revisión de alcance siguiendo la metodología JBI.
  • Se buscaron literatura revisada por pares y literatura gris (2000-2024) en múltiples bases de datos y sitios web de organizaciones.
  • Se incluyeron fuentes que ofrecían información conceptual/metodológica sobre el muestreo de MC; se excluyeron estudios empíricos de MC.

Principales resultados:

  • Diez fuentes incluidas, centradas en el muestreo para la toma de decisiones, no para la inferencia estadística.
  • Los métodos de no probabilidad (juicio, intencional, por conveniencia) dominaron debido al ajuste contextual y la factibilidad.
  • Se observaron limitaciones como sesgos y una guía poco clara sobre el tamaño de la muestra, con pocos marcos de acción.

Conclusiones:

  • El muestreo de MC equilibra el pragmatismo con el rigor estadístico.
  • Existe la necesidad de una guía más clara y adecuada para el muestreo de MC.
  • Una guía mejorada puede respaldar decisiones de muestreo transparentes, sensibles al contexto y metodológicamente sólidas.