Addressing oral health-related quality of life of the elderly – A Oral health promotion approach
Bibliographic record
Abstract
Abstract Background Older people are representative of the vulnerable population. According to census 2011, the proportion of elderly in India is 8.6%. Owing to social transformation and evolving lifestyles, the number of old age homes is increasing rapidly with institutionalized elderly having poorer oral health status. This study was conducted to assess the situation among the sample of institutions meant for the elderly and to plan relevant intervention, policy development aligning with principles of health promotion to improve the oral health quality of life. Methods A mixed-method study was adopted after approval from the institutional ethical board. To plan an appropriate intervention, situation analysis was done by conducting in-depth interviews, administrating questionnaires and direct observation. The intervention was planned based on the data obtained by building healthy public policy by a memorandum of understanding (MoU) between institutions, distribution of oral hygiene aids, oral health education, and demonstration of oral hygiene techniques. Institutionalized elderly selected for intervention in an old age home were 82. At baseline, oral health quality of life was assessed using the Geriatric Oral Health Assessment Index (GOHAI). After 3 months follow-up, Paired t-tests were used to compare baseline and follow-up data. Results Qualitative data on thematic analysis revealed that administrators highlighted on fiscal, economic, administrative, personnel, ethical aspects of policy instrument and type of oral health care services to be provided. Quantitative results showed that mean and SD for the pre and post GOHAI score was 1.6 ± 0.41 and 3.06 ± 0.80 (P < 0.001) Conclusions Oral health promotion approach to address challenges and issues at the community level appears a more promising approach as it facilitates a systematic process and more comprehensive. Policy initiatives with stakeholders brought a sustainable improvement of oral health quality of life. Key messages Ottawa charter model oral health promotion based intervention with the coordination of stakeholders helps in improving functional ability and intrinsic capacity of institutionalized elderly. To improve the quality of life among institutionalized elderly there is a need for transformation of health systems away from disease based curative models to comprehensive health care models.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".