Voicing Beliefs on Global Leadership for Dentistry
Bibliographic record
Abstract
BACKGROUND: Dental leadership in different models of care is not well documented, and therefore the objectives of this study were to explore how dental leaders develop their own leadership and how they engage others to increase access to oral health services as well as to describe perceived challenges in developing coalitions for promoting oral health care. METHODS: We adopted a qualitative descriptive research methodology. We recruited dental leaders using a purposeful sampling approach and a snowball technique. Data were collected using a remote digital platform; we organised semi-structured interviews based on the LEADS conceptual framework. Saturation was reached after 11 interviews. Data analysis included the following iterative steps: decontextualisation, recontextualisation, categorisation, and data compilation. The analysis was performed manually, assisted by the use of QDA Miner software. RESULTS: Fourteen dental leaders participated in the study. Our analysis revealed 3 overarching themes: (I) lead self, with 3 subthemes: leadership insights; leadership traits; opportunity-role model dyad; (II) leadership strategies; and (III) challenges in leadership development, with 3 subthemes: limited engaged practice and workforce, valorise the image of dentistry, and lack of leadership training. CONCLUSIONS: Our research findings showed that, despite a limited scope of leadership in dentistry, the dental leaders recognise its importance and acknowledge the need for formal training and mentorship at different levels. This study identified challenges in dental leadership development that could further orient dental education programmes and support the implementation of evidence-based, high-quality, and efficient oral health services.
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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 | 0.012 | 0.019 |
| 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 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".