Evidence map›Paper›PMID 31908634›Full record

ArticleJournal of Taibah University Medical Sciences2019

Translation of learning objectives in medical education using high-and low-fidelity simulation: Learners' perspectives.

Katarzyna A Naylor, Kamil C Torres

Abstract read
In one paragraph

Article in Journal of Taibah University Medical Sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Katarzyna A NaylorDepartment of Didactics and Medical Simulation, Medical University of Lublin, Lublin, Poland.
Kamil C TorresDepartment of Didactics and Medical Simulation, Medical University of Lublin, Lublin, Poland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThe mastering of learnt procedures by medical students is triggered by numerous elements, including the ability to understand educational goals for specific tasks. In this study, the authors investigated the processes for identifying learning objectives set forth by medical students and the possibility of the chosen simulation fidelity influencing this ability in Basic Clinical Skills and Elderly Medicine courses at the Medical University of Lublin.

methodsA total of 121 medical students assessed the extent to which learning objectives were implemented in two courses with high- and low-fidelity simulation. Using an online survey with closed-ended questions, a list of learning objectives assigned to the courses was sent to participants. The authors evaluated how the courses were generally assessed in terms of their substantive value and general applicability. The Spearman rank correlation (Spearman's rho), χ2, and descriptive statistics were used for investigating research problems.

resultsStudents correctly identified established learning objectives embedded in the courses and positively assessed both courses. Participants' affirmative opinions were related to the high substantive value of both courses.

conclusionsTeachers and course creators could benefit from students' feedback about the clarity of learning objectives. The application of some of their ideas would promote a student-centred approach in medical simulation. This approach could be considered input for task selection and optimisation of learning.

Indexed as

High-fidelity simulationLearning objectiveLow-fidelity simulationMedical educationUndergraduate medical students

Identifiers

PMID31908634
PMCPMC6940622

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.