Evidence map›Paper›PMID 40384806›Full record

ArticleMedEdPublish (2016)2024

Twelve tips for final year medical students undertaking clinical assessment.

Bunmi S Malau-Aduli, Richard B Hays, Shannon Saad, Karen D'Souza

Abstract read
In one paragraph

Article in MedEdPublish (2016), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Bunmi S Malau-AduliSchool of Medicine and Public Health, The University of Newcastle, Callaghan, New South Wales, Australia.ORCID https://orcid.org/0000-0001-6054-8498
Richard B HaysJames Cook University, Townsville, Queensland, Australia.ORCID https://orcid.org/0000-0002-3875-3134
Shannon SaadNSW Virtual Hospital, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-0423-5478
Karen D'SouzaDeakin University, Burwood, Victoria, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Clinical assessors in pre-registration examinations have been shown to make decisions about student performance by drawing on two overlapping, yet slightly different perspectives: achieving academic learning outcomes, and contributing to clinical workplace function. The implication for senior medical students is that they should be aware that in 'final' clinical assessments they may be judged from both academic and workplace perspectives, where the emphasis may be on how well the candidate would fit into a clinical team, demonstrating reliability, trustworthiness, teachability and 'safety'. Methods: This article presents 12 tips for how senior medical students may demonstrate progress towards achieving 'work readiness', and so improve performance in assessments close to graduation. Results: Clinical assessors may include judgment of how well the candidate might work as a junior member of a clinical team, particularly when candidates perform at the borderline level and where assessors are more experienced. This judgment is based on an impression of the student's demonstration of reliability, trustworthiness, patient safety and teachability. While the underpinning theory was explored in final OSCEs, the suggestions may also be relevant to workplace-based clinical learning and assessment. Conclusions: Senior medical students should prepare for clinical assessments that will consider how they respond to professionally challenging workplace scenarios as well as essential knowledge and skills.

Indexed as

Clinical assessmentDecision-makingOSCEProfessionalismworkplace-based assessment

Identifiers

PMID40384806
PMCPMC12082069

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Registered trials

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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.