Children’s Perceptions of Dental Experiences and Ways to Improve Them
Bibliographic record
Abstract
Children’s Perceptions of Dental Experiences and Ways to Improve ThemMaster of Science, 2023 Melika Modabber Paediatric Dentistry (Faculty of Dentistry), Univeristy of Toronto AbstractA qualitative study was conducted to explore children’s perceptions of their dental experiences and the acceptability of the CARD™ (C-Comfort, A-Ask, R-Relax, D-Distract) system, as adapted for the dental setting. Semi-structured virtual interviews were conducted from a purposive sample receiving dental care at the Paediatric Dental Clinic in University of Toronto. Deductive data analysis was performed using a Person-Centered Care framework (PCC). Twelve children (7 males) aged 8-12 years participated. Four themes were identified: (1) establishing a therapeutic relationship, (2) shared power and responsibility, (3) getting to know the person, and (4) empowering the person. Children emphasized the importance of clinic staff characteristics and communication skills. They expressed a desire to have an active role in their care decisions and reflected on their need for pre-operative education and parental presence. Children also felt that the modified CARD™ system was an effective tool to facilitate self-advocacy and optimize their dental experience.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".