Public Willingness to Participate in and Share Three-Dimensional (3D) Image Data from Orofacial Research: Results from a Preliminary Online Survey of Scottish Public Opinion
Bibliographic record
Abstract
Background: Orofacial traits are influenced by a large number of loci; requiring large population-based studies for their detection. Public willingness to participate in and share three-dimensional (3D) image data from orofacial genetic research is essential to ensure feasible implementation of these studies. This preliminary study aims to explore the Scottish public’s willingness to participate in and share 3D image data from orofacial genetic research, providing baseline data for future research. Methods: The online survey was administered from the 14th June–14th July 2017. Multinomial logistic regression was used to test for association with willingness to participate in and share image data from orofacial genetic research, with baseline demographics and prior knowledge of orofacial research as covariates. Results: A total of 82 members of the Scottish public were recruited. The majority of respondents (83%) would participate in a research project that involves the collection of facial images. Of those who would participate, most (93%) would share their data outside of the UK. Fewer (3%) would only share their data within the UK. Education level and prior knowledge of the oral-systemic health relationship were associated with respondent’s willing ness to participate. Education level was also associated with respondent’s willingness to share image data. Conclusions: This preliminary study indicates a high level of willingness among the Scottish public to participate in and share data from orofacial genetic research. The findings suggest that education level and prior knowledge of oral-systemic health influence participation, highlighting the need for targeted public engagement in orofacial genetic research. These results lay the groundwork for larger, more diverse studies to further explore these associations.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.006 | 0.001 |
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".