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	<title>consensus-building in public health indicator selection &#8211; Science</title>
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	<title>consensus-building in public health indicator selection &#8211; Science</title>
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		<title>Expert Delphi Study Defines 45 Practice-Learning Indicators for Chinese MPH Programmes</title>
		<link>https://scienmag.com/expert-delphi-study-defines-45-practice-learning-indicators-for-chinese-mph-programmes/</link>
		
		<dc:creator><![CDATA[Courtney Benton]]></dc:creator>
		<pubDate>Tue, 20 Oct 2026 07:58:44 +0000</pubDate>
				<category><![CDATA[Science Education]]></category>
		<category><![CDATA[China]]></category>
		<category><![CDATA[Chinese Master of Public Health program quality indicators]]></category>
		<category><![CDATA[conditions]]></category>
		<category><![CDATA[consensus-building in public health indicator selection]]></category>
		<category><![CDATA[Delphi method]]></category>
		<category><![CDATA[development of public health training assessment tools]]></category>
		<category><![CDATA[Evaluating]]></category>
		<category><![CDATA[evidence-based framework for public health education]]></category>
		<category><![CDATA[expert Delphi study on public health education]]></category>
		<category><![CDATA[fuzzy Analytic Hierarchy Process]]></category>
		<category><![CDATA[Master of Public Health]]></category>
		<category><![CDATA[Medical Education]]></category>
		<category><![CDATA[multidimensional evaluation of public health practice learning]]></category>
		<category><![CDATA[policy analysis of public health education standards]]></category>
		<category><![CDATA[practice training supervision and assessment in public health]]></category>
		<category><![CDATA[practice-based]]></category>
		<category><![CDATA[practice-based learning]]></category>
		<category><![CDATA[practice-based learning indicators in Chinese MPH programs]]></category>
		<category><![CDATA[programme evaluation]]></category>
		<category><![CDATA[public health education]]></category>
		<category><![CDATA[public health practice evaluation framework]]></category>
		<category><![CDATA[stakeholder expert opinions on public health training]]></category>
		<category><![CDATA[statistical methods in expert consensus studies]]></category>
		<category><![CDATA[student supervision]]></category>
		<guid isPermaLink="false">https://scienmag.com/?p=317008</guid>

					<description><![CDATA[A two-round Delphi study involving 18 to 20 experts produced 45 indicators for evaluating practice-based learning in Chinese Master of Public Health programmes.]]></description>
										<content:encoded><![CDATA[<p>An expert-informed framework for evaluating practice-based learning in Chinese Master of Public Health programmes has identified 45 indicators across three dimensions: Input, Process and Output. Developed through document review, interviews and two Delphi rounds, the framework assigns the greatest overall weight to Process, reflecting the study’s emphasis on how practical training, supervision and assessment are organised. It is a framework-development study based on evidence and expert judgments, not a test of whether any particular arrangement improves student or public health performance.</p>
<p>Researchers began with policy and institutional documents, published research and interviews with 20 public health professionals in Hebei Province. These sources informed 49 candidate indicators concerning practice-based learning. Two groups of experts then assessed each candidate’s importance, scientific soundness and feasibility through Delphi rounds involving 20 experts in the first round and 18 in the second. The researchers summarised ratings descriptively and used tie-corrected Kendall’s W to assess concordance in the experts’ relative rankings.</p>
<p>The second-round mean ratings ranged from 3.89 to 4.67 across the three assessment dimensions, while every coefficient of variation was no greater than 0.25. Expert agreement on ranking was low: Kendall’s W was 0.0529 for importance, 0.0569 for scientific soundness and 0.0474 for feasibility, and all associated P values exceeded 0.05. The results therefore indicate that experts gave generally high ratings to the retained indicators without consistently ranking them in the same order.</p>
<p>After the Delphi process, the framework contained three primary dimensions, nine secondary domains, 19 tertiary subdomains and 45 fourth-level indicators. A fuzzy analytic hierarchy process assigned Process the largest primary weight, at 0.481, followed by Input at 0.266 and Output at 0.253. The global weights were based on pairwise judgments made by the first author, with calculations jointly checked by the first and corresponding authors; products of local weights were then normalised to obtain global indicator weights. These values represent the weighting model’s priorities rather than measured effects on learning outcomes.</p>
<p>At the indicator level, specification of practice-mentor guidance time received the highest normalised global weight, 0.080508. Dual-qualified faculty ranked next, followed by four indicators with equal weights concerning practical training and assessment. The framework consequently provides a structured set of candidate conditions for reviewing how Chinese MPH programmes organise practical learning, including supervision and assessment within the wider degree. Because the study did not test implementation across programmes or students, its authors conclude that empirical evaluation is needed to determine whether the arrangements are delivered as intended and associated with students’ public health performance.</p>
<p><strong>Subject of Research:</strong> Expert-informed evaluation of practice-based learning conditions in Chinese Master of Public Health programmes</p>
<p><strong>Article Title:</strong> Evaluating the conditions for practice-based learning: an expert-informed Input–Process–Output framework for Master of Public Health programmes in China</p>
<p><strong>Article References:</strong> Tang, L., Shi, H., Li, B., Lan, J., &amp; Tian, J. (2026). Evaluating the conditions for practice-based learning: an expert-informed Input–Process–Output framework for Master of Public Health programmes in China. <em>BMC Medical Education</em>. <a href="https://doi.org/10.1186/s12909-026-10429-x" rel="noopener noreferrer">https://doi.org/10.1186/s12909-026-10429-x</a></p>
<p><strong>Image Credits:</strong> AI Generated</p>
<p><strong>DOI:</strong> <a href="https://doi.org/10.1186/s12909-026-10429-x" rel="noopener noreferrer">10.1186/s12909-026-10429-x</a></p>
<p><strong>Keywords:</strong> public health education, Master of Public Health, China, practice-based learning, Delphi method, student supervision, programme evaluation, fuzzy analytic hierarchy process, medical education, Evaluating, conditions, practice-based</p>
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