Exploring the role of the dental hygienist in reducing oral health disparities in Canada: A qualitative study
Bibliographic record
Abstract
OBJECTIVES: Reducing oral health disparities has been an ongoing challenge in Canada with the largest burden of oral disease exhibited in vulnerable populations, including Aboriginal people, the elderly, rural and remote residents, and newcomers. Dental hygienists are a unique set of professionals who work with and within communities, who have the potential to act as key change agents for improving the oral health of these populations. The purpose of this qualitative study was to explore, from the dental hygiene perspective, the role of dental hygienists in reducing oral health disparities in Canada. METHODS: Dental hygienists and key informants in dental hygiene were recruited, using purposeful and theoretical sampling, to participate in a non-directed, semi-structured one-on-one in-depth telephone interview using Skype and Call Recorder software. Corbin and Strauss's grounded theory methodology was employed with open, axial, and selective coding analysed on N-Vivo Qualitative software. RESULTS: The resulting theoretical framework outlines strategies proposed by participants to address oral health disparities; these included alternate delivery models, interprofessional collaboration, and increased scope of practice. Participants identified variation in dental care across Canada, public perceptions of oral health and dental hygiene practice, and lack of applied research on effective oral health interventions as challenges to implementing these strategies. CONCLUSION: The research confirmed the important role played by dental hygienists in reducing oral health disparities in Canada. However, due to the fragmentation of dental hygiene practice across Canada, a unified voice and cohesive action plan is needed in order for the profession to fully embrace their role.
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 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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.027 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".