COVID-19-Related School Closures and Caries Risk in Canadian Children.
Bibliographic record
Abstract
BACKGROUND: Despite increased efforts to improve the health of those with temporomandibular disorder (TMD), the focus remains on medical knowledge rather than patients' opinions and needs regarding quality of treatment and pain management. OBJECTIVES: We aimed to identify what TMD patients want their dentists to know and do. METHODS: Open-ended questions were used to understand the perspectives of 6 TMD patients. Two researchers examined the transcripts using interpretive phenomenological analysis. FINDINGS: TMD participants consistently stressed the need for their dentists to listen and provide them with more advice and information to cope with TMD conditions. They also noted the need for dentists to be skilled in communications, particularly maintaining respectful doctor-patient relations and interpersonal communication. CONCLUSIONS: Health care providers must acquire practical communication skills and expand their knowledge of TMDs to better support their patients. Improving relations between doctors and their TMD patients could result in positive health outcomes. The implications of this study will be to decrease medical crises and expensive interventions, provide better assistance to patients and refer them to other necessary health care professionals, an approach that will lead to lower care costs, more satisfaction and higher quality of life.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".