Assessment of metacognition levels: A cross-sectional study among dental professionals in Pakistan
Bibliographic record
Abstract
Background: Metacognition plays a pivotal role in learning, particularly in dentistry, enabling individuals to effectively manage their cognitive processes and identify areas for improvement. Objective: To evaluate the degree of metacognition among dental professionals at various phases of their careers. Methods: A cross-sectional study was conducted, using a non-probability sampling technique in private dental hospitals of Lahore from April to June 2024. A total of 320 dental professionals consented to participate. Data collection involved a semi-structured questionnaire and the Metacognitive Assessment Inventory (MAI), comprising two overarching domains – knowledge about cognition and regulation of cognition, each with three and five subdomains, respectively. Data were analysed using SPSS version 25. Descriptive statistics were computed, and one-way ANOVA and post hoc analysis were used to evaluate group differences. A p-value of <0.05 was considered statistically significant. Results: Consultants demonstrated significantly higher total cognitive knowledge scores than house officers (16.4 versus 8.5, respectively). This trend extended across subdomains, including procedural, conditional, and declarative knowledge. Similarly, consultants outperformed other participants in cognitive regulation (32.9 versus 17.6, respectively), particularly in planning and evaluation. Subdomains such as information management, communication, and debugging strategies were significantly higher in consultants than in graduate dentists, house officers, and postgraduates. General dentists had the highest cognition score among all the others (p<0.05). Conclusion: The metacognition level of consultants and general dentists was highest among dental professionals. It underscores the importance of fostering self-learning among dental professionals, particularly through developing their metacognitive abilities.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".