Exploration and Practice of the First Clinical Medical Postdoctoral Program in China: Retrospective, Nonrandomized, Controlled Study
Bibliographic record
Abstract
Background: To further optimize the clinical and scientific training of high-level doctoral graduates, the Office of the National Postdoctoral Administration launched a clinical postdoctoral program in 2015. This program provides postdoctoral clinical medicine trainees with 3 years of individualized, intensive training through a full mentorship system, interdisciplinary collaboration, and a multiteam teaching platform. Objective: This study aimed to compare the effectiveness of this novel clinical postdoctoral training program against conventional doctoral training, using specific, quantifiable metrics. Our primary research questions were as follows: (1) Does the program lead to superior clinical performance, as measured by theoretical examination scores and Case Mix Index (CMI)? (2) Does it enhance scientific research productivity, measured by publication output and success rates in procuring provincial and national NSFC (National Natural Science Foundation of China) funds? (3) Are there differences in teaching capacity and overall career advancement? Methods: This was a retrospective, nonrandomized controlled study. Doctoral graduates who entered the hospital for standardized residency training between 2015 and 2019 were enrolled and divided into a postdoctoral training group (n=23) and a doctoral training group (n=106). Results: The postdoctoral group demonstrated significantly higher clinical performance, as indicated by higher theoretical examination scores (445.70, SD 14.67 vs 435.12, SD 15.29; P=.003) and a higher median CMI (1.14 vs 0.92, P=.03), reflecting greater ability to manage complex clinical cases. In terms of research productivity, the postdoctoral group outperformed the doctoral group in the number of published papers (2.35, SD 2.39 vs 1.11, SD 1.47; P=.002) and the proportion of approved provincial-level NSFC projects (47.83% vs 17.00%, P=.001). However, no significant differences were observed in the acquisition of national-level NSFC funding (60.87% vs 44.34%, P=.15), teaching capacity (0.22, SD 0.518 vs 0.10, SD 0.306; P=.16), or overall competency indicators such as the rate of professional title promotion (100% vs 94.34%, P=.53) and the attainment of a master's-degree supervisor qualification (13.04% vs 5.67%, P=.42). Conclusions: The clinical postdoctoral training program demonstrates promising effectiveness in enhancing both clinical performance and scientific innovation among medical trainees. These findings support the value of integrating clinical practice, research, and mentorship in advanced postgraduate medical education and suggest that this model is worth promoting in more medical institutions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".