A scoping review on dental clinic accessibility for people using wheelchairs
Bibliographic record
Abstract
AIMS: We aimed to explore the scientific literature on accessible dental clinics for wheelchair users. More specifically, we sought out literature addressing how the human environment and physical space shape the dental services of accessible dental clinics. METHODS: We conducted a scoping review (May 2019) in Embase, PubMed, Web of Science, and the Avery index of architectural Periodicals (3994 articles). We followed Arksey and O'Malley's recommended procedures; after screening, we retained 17 articles. We performed a critical appraisal, followed by thematic content analyses of extracted data. RESULTS: The articles originated mainly from the United States and United Kingdom. Only three reported original research. We illustrated the results within a three-step dental care pathway cycle. In each step, the interaction between accessibility of the physical and human environments (ie, the layout/design of the clinic and the attitudes and skills of the dental professional, respectively) contributed to the overall accessibility. We also found that empirical evidence on clinics' accessibility was lacking: many articles resorted to broad "one size fits all" recommendations and fragmented information on accessibility. Finally, the voice of wheelchair users was missing. CONCLUSION: There are knowledge gaps in terms of dental clinics' accessibility. We thus invite researchers to conduct original studies with dental professionals, wheelchair users, and their caregivers.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".