Correlation of Chronotype (Lark versus Night Owl Status) with Mind-Set and Effect of Chronotype on Examination Performance in Veterinary School
Bibliographic record
Abstract
(productive late in the day). Society, including education, schedules work at times that generally favor larks. The goals of this study were to (a) define our student population regarding mind-set and chronotype, (b) examine the relationship between chronotype and mind-set score, and (c) examine the relationship between lark score and examination score with examinations offered at varying times of day. The null hypotheses were that there would be no relationships between these variables. If the hypotheses were not proven, this information would be used to educate students about personal management to optimize academic success and to help the college determine if scheduling or other variations in examination implementation could be altered to permit students to demonstrate best their knowledge and skills. There were 184 participants from the classes of 2020-2022. Overall, there were few definite night owls or larks, with 55% of participants categorized as neither. Overall, 78% of students had either a strong growth mind-set or a growth mind-set with some fixed ideas. No meaningful association between chronotype and mind-set score was observed. There was neither a significant main effect for chronotype nor a significant interaction with start time for examinations. Scheduling of examinations in the early morning did not negatively impact student performance based on chronotype in this study.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".