ArticlePerspectives on medical education2023
Using Self-Regulated Learning Microanalysis to Examine Regulatory Processes in Clerkship Students Engaged in Practice Questions.
Article in Perspectives on medical education, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 5 citations in OpenAlex.
- Bridging theory and practice: Experiences of diagnostic radiography students during clinical training in resource-constrained settings.Journal of medical radiation sciences · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
22 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Self-regulated learning is a cyclical process of forethought, performance, and self-reflection that has been used as an assessment tool in medical education. No prior studies have evaluated SRL processes for answering multiple-choice questions (MCQs) and most evaluated one or two iterations of a non-MCQ task. SRL assessment during MCQs may elucidate reasons why learners are successful or not on these questions that are encountered repeatedly during medical education. Methods: Internal medicine clerkship students at three institutions participated in a SRL microanalytic protocol that targeted strategic planning, metacognitive monitoring, causal attributions, and adaptive inferences across seven MCQs. Responses were transcribed and coded according to previously published methods for microanalytic protocols. Results: Forty-four students participated. In the forethought phase, students commonly endorsed prioritizing relevant features as their diagnostic strategy (n = 20, 45%) but few mentioned higher-order diagnostic reasoning processes such as integrating clinical information (n = 5, 11%) or comparing/contrasting diagnoses (n = 0, 0%). However, in the performance phase, students' metacognitive processes included high frequencies of integration (n = 38, 86%) and comparing/contrasting (n = 24, 55%). In the self-reflection phase, 93% (n = 41) of students faulted their management reasoning and 84% (n = 37) made negative references to their abilities. Less than 10% (n = 4) of students indicated that they would adapt their diagnostic reasoning process for these questions. Discussion: This study describes in detail student self-regulatory processes during MCQs. We found that students engaged in higher-order diagnostic reasoning processes but were not explicit about it and seldom reflected critically on these processes after selecting an incorrect answer. Self-reflections focused almost exclusively on management reasoning and negative references to abilities which may decrease self-efficacy. Encouraging students to identify and evaluate diagnostic reasoning processes and make attributions to controllable factors may improve performance.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.