Exploring mindfulness and artworks/drawings to predict dental students’ performance
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To explore and assess self-reported trait mindfulness and artwork/drawings as tools to predict students' performance. METHODS: This longitudinal study explored whether year 2 dental students' artwork/drawings produced during the first week of a preclinical endodontics course and Mindfulness Attention Awareness Scale (MAAS) scores could be used as a predictor of performance (grades/rank) at the end of the course. A convergent design of mixed methods approaches was used to integrate the quantitative and qualitative datasets. Qualitative analysis consisted of a multilayered process of thematic analysis of artwork/drawings that was used to generate codes, categories, and themes-according to lower and higher students' grades. Quantitative analysis consisted of statistical correlation between mindfulness scores and final grades. Findings were independently analyzed and further merged to answer our research question. RESULTS: The bivariate analysis found nonsignificant relationship between students' grades/rank and mindfulness scores: Pearson's correlation r = -0.097 (p = 0.578) and Spearman's correlation rho = 0.120 (p = 0.494). Codes, categories, and themes resulting from graphical data collected from the artwork/drawings strongly suggested that the higher students' grades group depicted solutions to deal with negative feelings/emotions and presented traits of confidence to reach goals. Artworks produced from students with lower grades left questions, such as in relation to competency in dentistry, unanswered, but at the same time, they seemed to perceive everything as emotion related. Upon merging the findings, we recognized more image components suggestive of positive feelings exuding from the artworks/drawings of higher grades group; but an increase in mindfulness was not associated with increase (or decrease) in final grade. CONCLUSION: Feelings/emotions represented in the artwork/drawings produced in the beginning of the course predicted students' performance at the end of the course; however, self-reported trait mindfulness was not correlated with performance.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".