What makes a dental clinic inclusive for people with disabilities?
Notice bibliographique
Résumé
Background: Dental professionals seem inadequately prepared to serve the 6.2 million Canadians who experience disability, especially wheelchair users. Some dental clinics, though, have modified their delivery models, improved their physical environments, and adopted welcoming attitudes toward them. Such accessible clinics are scarce and given their remarkable success in providing oral healthcare for persons with disabilities, we consider them as "champion” clinics. These champion clinics are important in our society because they provide a safe place for people with disabilities to receive quality dental care. They are also important for researchers, dental educators, and dental professionals because they can serve as models to make other clinics inclusive. It is thus essential to better understand how these clinics function, and how their human and physical environments have been organized to serve people with disabilities. This research aims to describe the characteristics of champion clinics and understand how their human and non-human (physical) environments contribute to their accessibility.Methodology: We conducted a focused ethnography to understand the culture of one information-rich champion clinic in Montreal. We organized semi-structured interviews with dental team members and patients. We also conducted observations focused on patients' pathways within the clinic, including their interaction with the clinic’s non-human (physical) and human environment. Data collection was guided by a conceptual framework known as the “model of competence,” which emphasizes the interaction of a person with the human and non-human environments of a caring facility. We then performed a thematic analysis with a combination of inductive and deductive coding. In this process, we were assisted by MAXDA software.Findings : Our analysis shows that the members of the dental team used person-centred approaches, which allowed them to overcome the physical and financial limitations of people with disabilities. More importantly, some deficiencies in the non-human environment were covered by the personnel’s attitudes and humanistic approaches. The clinic had a low-stress work environment shaped by three components: a practical non-human environment, team members’ humanistic and empathetic attitudes, and the dentist’s non-business mindset. Consequently, this low-stress work environment provided sufficient time and space for the dental team to pay attention to each patient’s needs and hold person-centred approaches.Conclusion: Several factors shape clinicians’ willingness to become inclusive: their level of empathy, social accountability, and their non-business mindset. By having the mentioned characteristics, dentists may overcome the financial and physical challenges of inclusivity using person-centred approaches. So, we suggest that dental schools emphasize on person-centred care in their curricula, and try to promote empathy, social accountability, and inclusion. Moreover, we suggest their admission committees modify their policies to admit students with higher levels of empathy and social accountability. In the end, we suggest healthcare systems to change the remuneration system and pay dentists more for providing service to people with disabilities. A more fundamental change would be taking dentistry to the public sector, and foster inclusive human and non-human environments
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,015 | 0,012 |
| Communication savante | 0,007 | 0,004 |
| Science ouverte | 0,003 | 0,006 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».