Exploring the views of relatives of frail elderly patients about participating in a geriatric dentistry program
Bibliographic record
Abstract
Elderly residents of long-term care facilities (LTC) have difficulty accessing dental services. Aiming to improve access for this population, the Geriatric Dental Program (GDP) was established by UBC Faculty of Dentistry in 2002. Within the GDP, elderly people receive fee-for-service dental care. The objective of this research was to explore whether accessing these services had an impact on the lives of the patients' relatives. Data was collected through semi-structured, face-to-face, audio-recorded interviews with family members of 12 GDP patients. A criterion sampling method was used to select the interviewed family. Interview transcription and data coding procedures were conducted following Saldaña. NVivo software was used to code and organize the transcripts. Data analysis followed a qualitative thematic analysis. Final analysis shows that patient relatives are worried about their relatives' oral health. They believe that it is difficult to find private dentists with geriatric expertise and to make appointments for their family members in private practice. In addition, they report that the GDP made their life less stressful and relieved the burden of setting up appointments for their relatives. Thus, it is expected that a similar program may positively affect the lives of patient relatives by improving access to dental care services.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".