Time loss due to dental problems and treatment in the Canadian population: analysis of a nationwide cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to quantify time loss due to dental problems and treatment in the Canadian population, to identify factors associated with this time loss, and to provide information regarding the economic impacts of these issues. METHODS: Data from the 2007/09 Canadian Health Measures Survey were used. Descriptive analysis determined the proportion of those surveyed who reported time loss and the mean hours lost. Linear and logistic regressions were employed to determine what factors predicted hours lost and reporting time loss respectively. Productivity losses were estimated using the lost wages approach. RESULTS: Over 40 million hours per year were lost due to dental problems and treatment, with a mean of 3.5 hours being lost per person. Time loss was more likely among privately insured and higher income earners. The amount of time loss was greater for higher income earners, and those who reported experiencing oral pain. Experiencing oral pain was the strongest predictor of reporting time loss and the amount of time lost. CONCLUSIONS: This study has shown that, potentially, over 40 million hours are lost annually due to dental problems and treatment in Canada, with subsequent potential productivity losses of over $1 billion dollars. These losses are comparable to those experienced for other illnesses (e.g., musculoskeletal sprains). Further investigation into the underlying reasons for time loss, and which aspects of daily living are impacted by this time loss, are necessary for a fuller understanding of the policy implications associated with the economic impacts of dental problems and treatment in Canadian society.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".