Predictive Value of Three Different Selection Methods for Admission of Motivated and Well-Performing Veterinary Medical Students
Bibliographic record
Abstract
In search for valid and reliable selection methods that predict applicants’ study motivation and academic performance during the 3-year bachelor’s program at the Dutch Faculty of Veterinary Medicine (FVM), this study aimed to (1) examine the predictive value of the three FVM selection methods for study motivation and academic performance (i.e., direct admission and weighted lottery based on secondary school grade point average [GPA], and selection based on non-cognitive criteria), and (2) examine whether type and level of study motivation could be of value regarding selection of well-performing students. Data from two cohorts at the FVM ( n = 186) were obtained, including mean summed scores on study motivation (using the Academic Motivation Scale [AMS] and additional items) and several academic outcome measures; among others, analyses of covariance (ANCOVA) were performed to examine differences between the three admission groups. Spearman’s correlations and linear regression were applied to examine the relationship between study motivation and academic performance. Lottery-admitted students demonstrated a stronger extrinsic motivation than selected students ( p < .05). Directly admitted students outperformed students from the other two admission groups on several academic outcome measures ( p < .05). Only the level of motivation was related to academic performance ( p < .05). According to the results, direct admission based on a high secondary school GPA in particular has predictive value for good academic performance during the 3-year bachelor’s program of the veterinary course. The type of motivation seems to be of no value regarding selection of well-performing students, whereas level of motivation might be a useful criterion for this purpose.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".