Oral health care in Canada--a view from the trenches.
Bibliographic record
Abstract
PURPOSE: Concern is increasing over the effect of lack of access to oral health care on the oral health, and hence general health, of disadvantaged groups. In preparation for a national symposium on this issue, key informants across Canada were canvassed for their perceptions of oral health services and their recommendations for improving oral health care delivery. This paper reports the results of that survey. METHOD: A questionnaire was constructed to address problems facing agencies with responsibility for meeting the oral health care needs of people receiving government assistance, the underhoused and the working poor. The survey was sent to 200 agencies, government and professional organizations. Data from the returned questionnaires were entered into a Statistical Package for the Social Sciences database and analyzed. Responses from Ontario were compared with those from the rest of Canada, those from government organizations were compared with others and results were examined by cultural nature of clients and by type of organization. RESULTS: In assessing the positive aspects of oral health care, 84% of respondents agreed that public programs were useful and 81% felt that dentists offer good care. However, 77% disagreed that preventive care is accessible and that access to dentists and dental specialists is easy. More Ontarians than others thought that there are few alternative settings for care delivery (95% vs. 83%) and that the poor feel unwelcome in dental offices (83% vs. 70%). The issues most commonly identified were the need for alternative delivery sites, such as community health centres where service delivery could be affordable, accountable and sustainable; the need for oral health to be recognized as part of general health; regulatory issues (e.g., expanding practice opportunities for non-dentist oral health care providers and removing restrictions on other dental health professionals in providing basic care to the financially challenged); and training. DISCUSSION: The survey helped to identify access and care issues across the country. There was considerable agreement that lack of access to dental care services is an important detriment to the oral and general health of many Canadians. Respondents believe that dental health is isolated from general health.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".