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Record W7161824434 · doi:10.82308/20179

What makes a dental clinic inclusive for people with disabilities?

2023· dissertation· en· W7161824434 on OpenAlexaboutno aff
Amirhossein Zargaran

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsnot available
Fundersnot available
KeywordsChampionThematic analysisQualitative researchParticipant observationDental clinicHealth careQuality (philosophy)Data collection

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score0.368

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0150.012
Scholarly communication0.0070.004
Open science0.0030.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.022
GPT teacher head0.374
Teacher spread0.352 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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