Transcultural skills content in a dental curriculum: a comparative study
Bibliographic record
Abstract
BACKGROUND: Australia has the highest proportion of immigrants in the world (24% of the population is overseas-born, compared to 22% in New Zealand, 19% in Canada and 12% in the USA). In this context, dental students have become increasingly diverse in a milieu where patients are derived from increasingly diverse backgrounds. AIM: The study aims to analyse the degree to which transcultural and communication skills content is currently embedded in the medical, physiotherapy and dental curricula at a major Australian university. MATERIALS AND METHODS: Undergraduate dental, medical and physiotherapy curricula were compared and critically assessed. Researchers considered the amount of transcultural and communication skills content, the number of formal contact hours for each course and the number of teaching staff involved. In addition, 21 interviews were conducted with staff at the three schools, who were involved in the curriculum development process. RESULTS: The medical and physiotherapy curricula had an explicit focus on transcultural and communication skills as a major and continuing element, delivered by teaching staff from a wide variety of academic and professional backgrounds. In contrast, the dental course showed an under-representation of transcultural and communication skills content which was taught by a limited number of staff from the School of Dental Science. CONCLUSIONS: In marked contrast to medical and physiotherapy curricula, transcultural and communication skills content had a low formal profile in the dental curriculum. A curriculum review process may be a positive step towards the development of a new training curriculum giving higher priority to transcultural and communication skills to support more effective workforce development.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".