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Record W4406985280 · doi:10.1093/bjsopen/zrae166

DiffErential attainment and Factors AssoCiated with Training applications and Outcomes (DE FACTO) for general surgery applications in the UK: retrospective study

2024· article· en· W4406985280 on OpenAlexaff
Sarika Grover, Siddarth Raj, Martina Spazzapan, Beth Russell, Harroop Bola, Noel E. Biju, Sachin Malde, Simon Fleming, Stella Vig

Bibliographic record

VenueBJS Open · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsDe factoRetrospective cohort studyMedicineDifferential (mechanical device)Training (meteorology)PsychologySurgeryPolitical scienceEngineeringGeography

Abstract

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The process of securing a core surgical trainee (CST) position in the UK and Ireland remains highly competitive due to a limited number of posts and an increasing number of applicants. CSTs are at least 2 years postgraduate from their medical degree. In 2023, 2539 candidates competed for 609 CST posts in the UK, resulting in a competition ratio of 4.17, an increase compared with previous years1. Upon completing CST become eligible to apply for Higher Surgical Training (ST3); however, the number of ST3 posts, particularly in general surgery, has not kept up with increasing demand. For instance, in 2021, 607 candidates applied for 136 ST3 positions, a competition ratio of 4.46. Despite recent interest in fostering diversity within surgical specialties, particularly in addressing sex disparities, there remains a paucity of data on the demographic, socioeconomic and educational factors influencing both application and success rates for ST3 positions. This study seeks to address these knowledge gaps by analysing demographic, socioeconomic and educational characteristics associated with CSTs who apply for and receive offers for ST3 in general surgery. This retrospective study analysed data from the UK Medical Education Database (UKMED) between January 2014 and December 2019. Primary outcomes were applying to and receiving an offer for a general surgery ST3 post on the first attempt. Adjusted logistic regression models were used to calculate odds ratios (ORs) for variables including age, sex, ethnicity, socioeconomic status (SES), domicile status, medical school type, Situational Judgement Test (SJT) scores and postgraduate exam success. This methodology has been previously utilized for other specialties2,3. Further information can be found in the Supplementary material. In this cohort of 1960 CSTs, the majority were male (63%), UK domiciled before medical school (81%) and attended Russell Group universities (69%). Of the 706 first-time applicants for general surgery ST3 positions, 477 (67.6%) received an offer. Female trainees were more likely to both apply and secure offers in general surgery (OR 1.82, 95% c.i. 1.51 to 2.20; OR 1.73, 95% c.i. 1.25 to 2.39), as were Russell Group graduates (OR 2.22, 95% c.i. 1.41 to 3.48) (Table 1). Higher SJT scores were also associated with offer success. Those who identified as black and minority ethnic (BME) were less likely to receive offers in comparison to their ‘white’ counterparts (OR 0.72, 95% c.i. 0.51 to 0.99; P = 0.044) (Table 1). Age and multiple specialty applications showed limited or no impact on application and offer rate (Table 1). Odds ratios and 95% confidence intervals (c.i.) for factors associated with applying to and obtaining a higher surgical training post in general surgery (n = 706) BME, black and minority ethnic; IMD, Index of Multiple Deprivation; N/A, not applicable; POLAR, ‘Participation of local areas’ which is metric of young people entering higher education at age 18 or 19 years of age; SJT, Situational Judgement Test. *Denotes adjusted odds ratio(s) as defined by the DAG graph (Supplementary Figure 1). †Denotes statistical significance that is P < 0.05. ‡Denotes cases where the presence of collinearity rendered precise estimation unattainable. Odds ratios and 95% confidence intervals (c.i.) for factors associated with applying to and obtaining a higher surgical training post in general surgery (n = 706) BME, black and minority ethnic; IMD, Index of Multiple Deprivation; N/A, not applicable; POLAR, ‘Participation of local areas’ which is metric of young people entering higher education at age 18 or 19 years of age; SJT, Situational Judgement Test. *Denotes adjusted odds ratio(s) as defined by the DAG graph (Supplementary Figure 1). †Denotes statistical significance that is P < 0.05. ‡Denotes cases where the presence of collinearity rendered precise estimation unattainable. In the context of adversity within a conventionally male-dominated profession, these findings highlight an encouraging trend. Specifically, there is an observed increase in both the propensity of female applicants applying for general surgery and the likelihood of receiving offers. This suggests a positive shift toward greater sex inclusivity within the field. Observational data from 2011 to 2020 corroborate our data, revealing a temporal increase in female representation among both registrars and consultants in general surgery4. Based on these findings, sex parity within the profession is projected to be achieved by 20284. Despite this, significant challenges persist for BME applicants. These include disproportionately lower pass rates in specialty examinations, an increased likelihood of receiving unsatisfactory annual review of competence progression outcomes, continued underrepresentation in surgical leadership roles and pervasive racial discrimination5. Evidence suggests that an SJT score between 35 and 44 was associated with improved odds. However, as of 2024, The United Kingdom Foundation Programme Office has replaced the SJT with a preference-informed allocation system, negating the impact of SJT performance as a differential predictor for application success. Overall, this mixed picture highlights the successes of sex-related initiatives but provides evidence suggesting ethnic disparity in UK general surgical training. Further high-quality, prospective research is needed as we work towards developing a diverse surgical workforce. The authors have no funding to declare. Source: UK Medical Education Database (UKMED) P134 extract generated on 16 June 2021. Approved for publication on 21 June 2024. The authors are grateful to UKMED for the use of these data. However, UKMED bears no responsibility for their analysis or interpretation. The data includes information derived from that collected by the Higher Education Statistics Agency Limited (HESA) and provided to the General Medical Council (HESA Data). Source: HESA Student Record 2002/2003 and 2017/2018 Copyright Higher Education Statistics Agency Limited. The Higher Education Statistics Agency Limited makes no warranty as to the accuracy of the HESA Data, cannot accept responsibility for any inferences or conclusions derived by third parties from data or other information supplied by it. S.G. and S.R. have contributed equally and should be recognized as joint first authors. The authors declare no conflict of interest. Supplementary Material is available at BJS Open online. Owing to restrictions on participant consent and data-sharing policies, the supporting data are not publicly available. Sarika Grover (Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Visualization, Writing—original draft, Writing—review & editing), Siddarth Raj (Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Visualization, Writing—original draft, Writing—review & editing), Martina Spazzapan (Formal analysis, Methodology, Software, Writing—review & editing), Beth Russell (Formal analysis, Methodology, Software, Writing—review & editing), Harroop Bola (Writing—original draft, Writing—review & editing), Noel Biju (Writing—original draft, Writing—review & editing), Sachin Malde (Supervision, Validation, Writing—review & editing), Simon Fleming (Supervision, Validation, Writing—review & editing) and Stella Vig (Supervision, Validation, Writing—review & editing)

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.999
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.064
GPT teacher head0.345
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainIncentives
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations3
Published2024
Admission routes1
Has abstractyes

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