Evidence map›Paper›PMID 35192479›Full record

ArticleClinical medicine (London, England)2021

Impact of changing medical workforce demographics in renal medicine over 7 years: Analysis of GMC national trainee survey data.

Matthew Pm Graham-Brown, Hannah K Beckwith, Simon O'Hare, Dominic Trewartha, Aine Burns, Sue Carr

Abstract read
In one paragraph

Article in Clinical medicine (London, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 2 pooled it
–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

3 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. 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

6 authors.

Matthew Pm Graham-BrownUniversity of Leicester, Leicester, UK, Glenfield Hospital, Leicester, UK, and University Hospitals of Leicester NHS Trust, Leicester, UK.
Hannah K BeckwithImperial College London, London, UK.
Simon O'HareGeneral Medical Council, London, UK.
Dominic TrewarthaGeneral Medical Council, London, UK.
Aine BurnsUniversity College London, London, UK and Royal Free Hospital, London, UK.
Sue CarrUniversity Hospitals of Leicester NHS Trust, Leicester, UK and General Medical Council, London, UK sue.carr1@gmc-uk.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Increasing numbers of doctors in training are taking career breaks, with burnout cited as a potential cause. This study analysed General Medical Council (GMC) national training survey data (renal medicine) to understand the impacts of changing workforce demographics on trainee outcomes and wellbeing. Increasing proportions of female, Black, Asian and minority ethnic (BAME), and international medical graduates are entering the workforce. Specialty exam pass rates have fallen and are lower for BAME and international medical graduates in renal medicine. Time to complete higher specialty training has increased for female trainees. Self-reported burnout rates for renal trainees were higher than other medical specialties and highest for male BAME trainees. Burnout was only partially mitigated by less-than-full-time working, but had no impact on progression, sick-leave or time out of training. It is important to recognise changes to the workforce and proactively plan to effectively support a more diverse group of trainees, to enable them to succeed and reduce differential attainment.

Indexed as

MedicinePhysiciansCareer ChoiceDemographyFemaleHealth PersonnelHumansMaleWorkforceethnicitymedical educationphysiciantrainingworkforce

Identifiers

PMID35192479
PMCPMC8313209

What OpenQuestion holds

Textmetadata
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.