Trust and Distrust in Dental Professionals: Patient Perceptions and Experiences
Bibliographic record
Abstract
PURPOSE: To investigate factors that contribute to trust in dentists among patients with socioeconomic barriers and explore how these factors influence patient decisions to undergo dental treatment. METHODS: Patients at the University of Toronto, Faculty of Dentistry, participated in one-on-one semi-structured interviews, answering open-ended questions about circumstances that led to the development and erosion of trust. Interviews were recorded, transcribed verbatim, and coded. Thematic analysis was used to organize data from transcripts and develop themes. RESULTS: Interviews were conducted with 25 patients (18 females, 7 males; age range 22-68 years). Four themes were developed: (1) Patients' perception of dentists' technical skills and reputation was important to building trust. Dentists perceived by patients to provide high-quality work, minimize pain, use current technology, and have positive endorsements led to trust. (2) Patients' perception of dentists' communication skills and empathy was important to building trust. Patients reported having trust in dentists who communicated treatment details, remained transparent, engaged them in decision-making, and prioritized their well-being. (3) Patients varied in how they associated trust with cost and their previous dental experience. (4) Trust influenced patient decisions to proceed with dental treatment. CONCLUSIONS: Trust is a dynamic component of the dentist-patient relationship that patients assess throughout their treatment-seeking process. Empathy and communication skills are modifiable attributes that dentists can develop to build their patients' trust. Dentists have less control over how patients perceive their competence. Patient trust in dentists fosters active engagement in treatment, while distrust can lead to switching dentists or avoiding treatment.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".