Person-Centered Dental Care Through the People’s Lens – A Qualitative Descriptive Study
Bibliographic record
Abstract
Objectives: The biomedical paternalistic approach in dentistry has been progressively replaced by a more balanced approach known as Person-centered care. Unlike other professions, there are only a few dentistry-specific person-centered models, and most were developed by researchers and dental practitioners without the input of people. Because it is important to gain people's perspective, this study aimed to understand people's expectations and preferences for their dental encounters and contribute to developing person-centered care in dentistry.Methods: We conducted a qualitative descriptive study. It was based on one-on-one, in-depth semi-structured interviews with twelve South Asian immigrant women in Montreal, Quebec, Canada. We adopted a sampling strategy that was ‘purposive’ and, more specifically, ‘homogenous sampling,’ as described, with the goal of understanding this population in-depth and obtaining information-rich cases pertaining to our research question. The interviews were conducted on Zoom and lasted for an average of 45 minutes. The interview guide was devised using Bedos et al. Q – list of “The Montreal-Toulouse Wheel of Patient’s Expectation for dental visits”, which is based on a biopsychosocial model of dental practice. These interviews were audio-recorded, transcribed, and analyzed thematically. Results: The participants highlighted the relevance of the “The Montreal-Toulouse Wheel of Patient’s Expectation for dental visits,” which includes four core components (Be understood; respected; provide enough time; share powers), and three components related to the clinical process (be informed and consent; be comfortable; co-construct treatment plan). This said, the participants emphasized having enough time during clinical encounters and working as a team with the dentist. Finally, the participants added the importance of having a warm and friendly relationship with the dental team to make their dental visits comfortable.Conclusion: This study improves our understanding of what people may expect in a person-centered dental encounter and contributes to advancing person-centered care in dentistry. It could be useful to dentists and their teams interested in adopting person-centered approaches. In addition, we hope the findings will inform the public about their rights and what they could expect when consulting dental professionals
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".