Exploration of Mental Readiness for Enhancing Dentistry in an Inter-Professional Climate
Bibliographic record
Abstract
Competencies required for dentistry go far beyond the academic or scientific spheres. They incorporate important mental readiness concepts at its core with an appropriate balance of operational readiness (i.e., technical, physical, mental readiness). The aim of this exploratory study was to investigate the importance of mental readiness for optimal performance in the daily challenges faced by dentists using an Operational Readiness Framework. One-on-one interviews were conducted with a select group of seasoned dentists to determine their mental readiness before, during and after successfully performing in challenging situations. Quantitative and qualitative analyses of mental readiness were applied. Study findings were compared with a Wheel of Excellence based on results from other high-performance domains such as surgery, policing, social services and Olympic athletics. The analysis revealed that specific mental practices are required to achieve peak performance, and the balance between physical, technical and mental readiness underpins these dentists' competency. Common elements of success were found-commitment, confidence, visualization, mental preparation, focus, distraction control, and evaluation and coping. This exploration confirmed many similarities in mental readiness practices engaged across high-risk professions. Universities, clinics and hospitals are looking for innovative ways to build teamwork and capacity through inter-professional collaboration. Results from these case studies warrant further investigation and may be significant enough to stimulate innovative curriculum design. Based on these preliminary dentistry findings, three training/evaluation tools from other professions in population health were adapted to demonstrate future application.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".