Relationships between Learning Strategies, Stress, and Study Success Among First-Year Veterinary Students During an Educational Transition Phase
Bibliographic record
Abstract
We investigated the relationships between stress, learning strategies, and study success among first-year veterinary students at the very beginning of their veterinary studies. The study was carried out during the first course on macroscopic anatomy (osteology), which students have in the past found to be exceptionally stressful. Students (N=45) completed a questionnaire concerning their self-reported views on stress and learning strategies, which were compared with their self-reported written-test scores. Participants who had previously gained university credits did not have significantly better test scores, but they achieved the learning goals with significantly less stress than other participants. Previous experience of university study helped students not only to adjust to a new type of course content and to achieve the learning goal of the osteology course, but also to cope with the stress experienced from taking concurrently running courses. Of the respondents who specifically named factors relating to self-regulation and modification of their learning strategy, all had gained prior credits. These students were able to use their study time efficiently and adjust their schedules according to the course demands.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".