European student wellness, stress, coping, support and perceptions about remote dental training during COVID‐19
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to compare wellness, stress, ability to cope, social support and perceptions about remote training amongst European dental students during COVID-19. METHODS: 1795 undergraduate dental students from six countries and eight dental schools participated. The anonymous survey collected data about different aspects in each of the following domains: wellness, stress, ability to cope, social support and perceptions about remote training. Complex multi-item scales were used for all domains. RESULTS: There were differences amongst countries in all the domains. Overall, student stress scores were lower than either their coping or support scores. The highest wellness score (mean ±sd) was observed in Romania: 62.5% ± 11.2% whilst the highest mean stress scores were observed in Albania: 46.3% ± 11.7% and Lithuania: 42.2% ± 13.8%. Overall, student stress and coping ability scores were lower and their support scores higher. About 10% of students did not have any support. In the linear multivariable regression analysis, significant predictors of wellness were being female (β = 0.073), not being in a graduating year (β = 0.059), having less stress (β = 0.222), ability to cope (β = 0.223) and having support (β = 0.179). The student positive perceptions about remote training were predicted by less stress (β = 0.080), coping (β = 0.182) and support (β = 0.057). CONCLUSIONS: Students varied in wellness, stress, coping, social support and perceptions of remote training. Also, there were significant differences amongst students from different countries. Coping was the best predictor of both student wellness and their positive perceptions about remote training.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".