Evidence map›Paper›PMID 28222710›Full record

ArticleBMC medical education2017

Predicting medical students who will have difficulty during their clinical training.

D L Jardine, J M McKenzie, T J Wilkinson

Abstract read
In one paragraph

Article in BMC medical education, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Observational
  4. Article
  5. Observational
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

3 authors.

D L JardineDepartment of General Medicine, Christchurch Hospital, University of Otago, Riccarton Ave 2, Christchurch, 8011, New Zealand. david.jardine@cdhb.govt.nz.
J M McKenzieDepartment of General Medicine, Christchurch Hospital, University of Otago, Riccarton Ave 2, Christchurch, 8011, New Zealand.
T J WilkinsonDepartment of General Medicine, Christchurch Hospital, University of Otago, Riccarton Ave 2, Christchurch, 8011, New Zealand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWe aimed to classify the difficulties students had passing their clinical attachments, and explore factors which might predict these problems.

methodsWe analysed data from regular student progress meetings 2008-2012. Problem categories were: medical knowledge, professional behaviour and clinical skills. For each category we then undertook a predictive risk analysis.

resultsOut of 561 students, 203 were found to have one or more problem category and so were defined as having difficulties. Prevalences of the categories were: clinical skills (67%), knowledge (59%) and professional behaviour (29%). A higher risk for all categories was associated with: male gender, international entry and failure in the first half of the course, but not with any of the minority ethnic groups. Professional and clinical skills problems were associated with lower marks in the Undergraduate Medical Admissions Test paper 2. Clinical skills problems were less likely in graduate students.

conclusionsIn our students, difficulty with clinical skills was just as prevalent as medical knowledge deficit. International entry students were at highest risk for clinical skills problems probably because they were not selected by our usual criteria and had shorter time to become acculturated.

Indexed as

Education, GraduateEducation, Medical, UndergraduateAdultClinical CompetenceEducational MeasurementFemaleHealth Knowledge, Attitudes, PracticeHumansMaleMinority GroupsNew ZealandPredictive Value of TestsProfessionalismSchool Admission CriteriaSchools, MedicalStudents, Medical

Identifiers

PMID28222710
PMCPMC5320727

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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.