Improving Person-Centered Access to Dental Care: The Walk-In Dental Encounters in Non-Emergency Situations (WIDENESS)
Bibliographic record
Abstract
BACKGROUND: We hypothesized that access to dental care could be improved by the conceptualization of a new type of consultation: The walk-in dental encounter for non-emergency situations (WIDENESS). The aim of this study was to assess patient perspectives regarding walk-in dental consultations, with a particular focus on non-emergency situations. METHODS: We followed a qualitative research approach using a semi-structured interview guide in a sample of random participants recruited from the dental department of the Toulouse University Hospital, France. We performed a thematic analysis of the interview transcripts. Data saturation was obtained after interviewing 11 participants. RESULTS: When asked about walk-in dental consultations, three main topics emerged: (1) Walk-in dental consultation in general is important for emergency situations, but WIDENESS did not correspond to any specific long-standing need from participants; (2) WIDENESS could be a way to improve access to oral care (facilitating access to care relative to time constraints, reduction of dentist-related anxiety, better overall follow-up for the care pathway, and the complementary nature of consultations with and without appointments); and (3) WIDENESS has some potential drawbacks-apprehension about long waiting times was mentioned by several participants. CONCLUSIONS: Participants found the idea of WIDENESS promising, despite spontaneously mentioned reservations, which constitute major challenges to its implementation.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.007 |
| 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".