Dental treatment needs in the Canadian population: analysis of a nationwide cross-sectional survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Nationally representative clinical data on the oral health needs of Canadians has not been available since the 1970s. The purpose of this study was to determine the normative treatment needs of a nationally representative sample of Canadians and describe how these needs were distributed. METHODS: A secondary analysis of data collected through the Canadian Health Measures Survey (CHMS) was undertaken. Sampling and bootstrap weights were applied to make the data nationally representative. Descriptive frequencies were used to examine the sample characteristics and to examine the treatment type(s) needed by the population. Bivariate logistic regressions were used to see if any characteristics were predictive of having an unmet dental treatment need, and of having specific treatment needs. Lastly, multivariate logistic regression was used to identify the strongest predictors of having an unmet dental treatment need. RESULTS: Most of the population had no treatment needs and of the 34.2% who did, most needed restorative (20.4%) and preventive (13.7%) care. The strongest predictors of need were having poor oral health, reporting a self-perceived need for treatment and visiting the dentist infrequently. CONCLUSIONS: It is estimated that roughly 12 million Canadians have at least one unmet dental treatment need. Policymakers now have information by which to assess if programs match the dental treatment needs of Canadians and of particular subgroups experiencing excess risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it