Preclinical dental students and their learning environment: A wellbeing perspective
Bibliographic record
Abstract
OBJECTIVES: Preclinical dental education can challenge students' wellbeing. These challenges are multifaceted and are experienced differently across student populations. However, an in-depth understanding of these challenges and the changes in the students' response to them remains limited. Therefore, this study aims to explore the wellbeing experiences of preclinical dental students and the strategies they developed to maintain their wellbeing over two time points: the academic years 2021/2022 and 2012/2013. METHODS: A case study was conducted using qualitative data from semi-structured interviews with preclinical dental students in 2021/2022. Data were thematically analyzed, and data from the Dental Student Study Habits Survey in 2012/2013 were later used for triangulation. RESULTS: Fifteen students participated in the interviews. Twenty-four students participated in the 2012/2013 survey in the beginning of the academic year. Three themes emerged-uncertain transitions, challenging interactions, and multifaceted impact and balancing strategies-along with various subthemes. Students in 2012/3013 reported high levels of stress, anxiety, overwhelm, and fatigue during dental school in comparison with other time points, and most students in both periods reported developing physical, phycological, and social strategies to maintain their wellbeing. The lack of time was found to be a barrier for practicing stress management strategies across the two time points. CONCLUSION: Students experience several challenges in preclinical dental education that negatively impacts their wellbeing and seem to persist over the past decade. It is important that dental education programs consider inclusive and nurturing teaching approaches in the learning environment to support students' wellbeing and performance.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".