Longitudinal Trajectories of Dental Attendance in Australian Adults
Bibliographic record
Abstract
Understanding how dental attendance evolves throughout life can inform targeted preventive health care policies by identifying key moments when people are more or less likely to seek dental care. Trajectory modeling of age and time trajectories takes a life course approach to understanding dental attendance, offering insights into both developmental perspectives (e.g., life stages) and structural perspectives (e.g., social position and health care systems) throughout the life course. This study used group-based trajectory modeling to identify (1) the age trajectories of dental attendance among Australian adults from young adulthood to retirement age and (2) the distinct time trajectories of dental attendance among Australian working-age adults. Data from the Household, Income and Labour Dynamics in Australia (HILDA) study was used to fit 2 trajectory models (age and time based). Age trajectories were fitted for individuals aged 15 to 64 y using dental attendance data from 3 time points: 2009, 2013, and 2017. Time trajectories were fitted for working-age adults (24–54 y) using data from 2009 to 2017 and descriptively analyzed by social characteristics. Dental attendance was classified as frequent (less than 2 y since the last visit) or infrequent (2 y or longer). Two distinct age trajectories emerged among participants ( N = 11,189): the mostly frequent (75.1%) and declining-infrequent group (24.9%). A sharp decline in the probability of being frequent attendees was observed between 15 and 20 y in a quarter of the population with no subsequent change. Four time trajectories were identified ( n = 7,033): consistently frequent (37.8%), consistently infrequent (8.9%), increasing attendance (22.2%), and declining attendance (31%). Descriptive analysis showed that age and social inequalities were evident in the trajectories. The findings emphasize the need for preventive health care policies that account for life-stage dynamics and their impact on attendance behaviors, in addition to improving structural factors.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".