Explanatory models in the interpretations of clinical features of dental patients within a university dental education setting
Bibliographic record
Abstract
Clinicians may acquire biased perceptions during their dental education that can affect decisions about treatment/management of dental decay. This study established explanatory models used by students to interpret clinical features of patients. It employed a stereotypical dental patient under standardised consultation conditions to identify the interpretation of oral health/disease features in the eyes of student clinicians. The study aimed to establish the perceptions of the patient as a client of the university dental clinic, as seen through the ideological lens of a formal Dental Education system. The discourse during simulated clinical consultations was qualitatively analysed to interpret values and concepts relevant to the assessment of restorative treatment needs and oral health status. Three constructs during the consultation were identified: the Dual Therapeutic Realms, the Choices Underlying Treatment Options, and the High-Risk Triad. Comparing these discourse components, the Patient Factors of the Bader and Shugars model for treatment decisions supported the existence of a core set of themes. It was concluded that certain consultation circumstances influenced the adequacy of diagnostic strategies, mainly by introducing loosely defined but highly specific socio-cultural biases ingrained in the Dental Education concepts and diagnostic/treatment needs systems.
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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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".