Physiognomy and teeth: An ethnographic study among young and middle-aged Hong Kong adults
Bibliographic record
Abstract
Objectives: To determine knowledge and beliefs about traditional physiognomy (judging an individual's character from their facial appearance) concerning teeth among young (17-26) and middleaged (35-44) Hong Kong adults. Methods: In a cross sectional ethnographical telephone survey, 400 adults were interviewed about 16 traditional physiognomy concerning teeth (in consultation with a Feng Shui specialist) Results: Most completed the interview (93%, 373) Over half the study group (63%, 234) claimed they had heard of aspects of physiognomy concerning teeth, and a quarter (24%, 88) believed in such ideologies. Variations in knowledge and beliefs were apparent among people of different age (P<0.01), gender (P<0.05), educational attainment (P<0.01), economic status (P<0.01), place of birth (P<0.01) and religion (P<0.01). Their knowledge and belief in aspects of physiognomy concerning teeth was also associated with reported use of dental services (P<0.01). Conclusion: Among young and middle-aged adults in Hong Kong, knowledge and beliefs concerning traditional physiognomy regarding teeth is strong, and socio-demographic variations exist in these perceptions. These findings have implications for all those involved in the delivery of dental care in multicultural societies and in raising cultural awareness about traditional health beliefs.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".