Evidence on selection tools predicting dropout is scarce, with no tool showing a statistically significant effect on dropout¶

Claim · All claims
Evidence · 1 study · quant-synthesis r2 · q3
Currency · newest study 2024 · not yet checked against its sources · not reviewed by a person · recheck by 2027: tool and model accuracy changes quickly

Subclaims¶

q3 i? Pooled dropout effect sizes (e.g., 0.205 for prior academic achievement, CI -0.162 to 0.572) had confidence intervals intersecting zero. → Huang Pin-Hsiang 2024

Evidence¶

Huang Pin-Hsiang 2024¶

Huang Pin-Hsiang, Arianpoor Arash, Taylor Silas, Gonzales Jenzel, Shulruf Boaz. (2024). Insights into undergraduate medical student selection tools: a systematic review and meta-analysis. Journal of Educational Evaluation for Health Professions. https://doi.org/10.3352/jeehp.2024.21.22

q3 · i? · quant-synthesis · r2

Random-effects meta-analysis dropout cells in Table 2 (e.g., academic achievement 0.205 [-0.162 to 0.572]; aptitude tests 0.082 [-0.395 to 0.559]) all had CIs crossing zero. The authors identify this as a research gap requiring urgent investigation.

"It is quite striking to see that very little is known about the prediction of (timely) completion (or dropout) by the selection tools. Obviously, this topic has not been sufficiently investigated despite timely completion being the most critical outcome of the medicine program"

Discussion¶

Related Claims¶

• Prior academic achievement predicts medical program academic performance with medium effect sizes (d=0.697 early program; d=0.619 end of program) and end-of-program clinical exam performance (d=0.545) — related
• Multiple mini-interviews significantly predict early clinical exams (d=0.417) and early academic results (d=0.195), while panel interviews show no significant predictive effects — related