Unmet health care needs of older people: prevalence and predictors in a French cross-sectional survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it