Unmet health care needs of older people: prevalence and predictors in a French cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: Unmet health care needs are associated with negative health outcomes, yet there is a paucity of data on this problem among older people. OBJECTIVE: To identify unmet health care needs and associated factors among older people in France. METHODS: This is a cross-sectional population study of people aged 70 years or older in which 2350 respondents were interviewed in 2008-10. During a standardized interview, a nurse examined health problems, functional abilities and use of health care resources. Unmet health care needs were defined as situations in which a participant needed health care and did not receive it. RESULTS: The mean age was 83.2 ± 7.4 years. Almost all participants reporting a chronic disease (98.6%) had consulted a physician in the previous 6 months. Unmet health care needs were found in 23.0% of the sample and mainly consisted of lack of dental care (prevalence of 17.7%), followed by lack of management of visual or hearing impairments (prevalence of 4.4% and 3.1%, respectively). Age was the main factor associated with unmet health care needs [compared with people aged 70-79: odds ratio80-89 years = 2.26 (1.70-3.03), odds ratio90 years and over = 3.85 (2.71-5.45)]. Other associated factors were regular smoking, homebound status, poor socioeconomic conditions, depression, limitations in instrumental activities of daily living and low medical density. CONCLUSION: Unmet health care needs affect almost one-quarter of older people in France. Efforts should be made to improve oral health and develop home care, especially for the oldest-olds.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".