The impact of integration of dental services on oral health in long‐term care: qualitative analysis
Bibliographic record
Abstract
OBJECTIVES: To qualitatively analyse how integration of dental service in long-term care (LTC) impacts residents and their oral health. BACKGROUND: Few studies have attempted to merge inductive and deductive data to clarify the significance of the complex psychosocial environment in LTC facilities. Understanding the subjective oral health experience of LTC residents in their social setting is key to uncovering behavioural patterns that may be limiting the oral care provided to LTC residents. MATERIALS AND METHODS: A cross-sectional study was performed involving 61 residents in three Ontario LTC facilities. Observations and reflective notes were recorded during open-ended interviews using a structured questionnaire to stimulate conversation topics. This ensured that each resident received the same prompting during the interview process. Inductive analysis was used to identify common patterns and themes within field notes and transcriptions. RESULTS: The major themes identified included oral hygiene, oral discomfort, general health, appearance, dental access, and denture related issues. Oral hygiene and discomfort were the dominating categories within the facilities. CONCLUSION: Two of the three LTC centres identified in this study failed to provide appropriate oral care for their residents. Future research needs to be directed at prospective studies assessing the effect of oral health education and mandatory dental examinations o entry within LTC centres utilising qualitative and quantitative analyses.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".