Oral health care in long‐term care facilities for elderly people in southern Brazil: a conceptual framework
Bibliographic record
Abstract
OBJECTIVE: To present a theoretical model for understanding oral health care for the elderly in the context of long-term care institutions (LTCI). METHODS: Open-ended individual interviews were conducted with the elderly residing in LTCI, their carers, nursing technicians and nurses, directors of care, dental surgeons and managers of public health services. A grounded theory methodological approach was adopted for data collection and analysis. RESULTS: The emerging core category revealed a basic social process: 'Promoting oral health care for the elderly based on the context of LTCI'. This process was composed of two contradicting yet correlated aspects: the oral health care does not minimise the poor oral epidemiological condition, and at the same time, there was a continued improvement in the oral care expressed by better care practices. These aspects were related to the: attribution of meaning to oral health, social determination of oral health, the ageing process, interactions established in the oral health care practices, oral health care management in LTCI, inclusion of oral health care into the political-organisational dimension and possibility of conjecturing better oral health care practices. CONCLUSION: The core concept of 'Promotion of oral health care for elderly people based on the context of LTCI' is capable of explaining the variations in the structure and process of LTCI, as well as in helping to understand the meaning of the oral health care practices for the institutionalised elderly.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".