Does academic interest play a more important role in medical sciences than in other disciplines? A nationwide cross-sectional study in China
Bibliographic record
Abstract
BACKGROUND: Research examining the effects of academic interest on students learning achievement across various disciplines, especially a comparison of the effects of academic interest between medical sciences and other disciplines, is still scarce. This study addressed this gap by answering 'does academic interest play a more important role in medical sciences than in other disciplines?'. METHODS: A retrospective cross-sectional study, based on a large project of the National Undergraduate Student Development Survey (NUSDS) conducted by the Ministry of Education of China and Peking University in 2014, was designed to explore the role of academic interest in medical sciences and other disciplines. The participants were resampled to better represent the national distribution of undergraduate students in terms of their demographic characteristics. Specifically, survey data from 54,398 undergraduate students from 87 Chinese universities and colleges were used to address our research questions. We then used the propensity score matching (PSM) model to estimate the effect of academic interest on academic achievement and to compare the effects across different disciplines. RESULTS: Academic interest had a significant positive impact on academic performance, with an effect size of 2.545 (p = 0.000). Specifically, the effect sizes for the disciplines of medical sciences, humanities, social sciences, natural sciences and engineering were 2.310 (p = 0.000), 2.231 (p = 0.000), 2.016 (p = 0.000), 3.840 (p = 0.000) and 2.698 (p = 0.000), respectively. The results show that no particular academic interest in medical sciences is needed to achieve academic success when compared with natural sciences and engineering programmes, but success in medical sciences requires more academic interest than success in humanities or social sciences. CONCLUSIONS: This study clarifies the effect of academic interest on undergraduates' academic achievement while controlling for their demographic characteristics and family factors. The results provide insights into the role of academic interest in academic performance across various disciplines and can inform the college admissions practices of both institutions and high school students in China.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".