Are older people worse off in 2040 regarding health and resources to deal with it? - Future developments in complex health problems and in the availability of resources to manage health problems in the Netherlands
Bibliographic record
Abstract
Introduction: Developing sustainable health policy requires an understanding of the future demand for health and social care. We explored the characteristics of the 65+ population in the Netherlands in 2020 and 2040, focusing on two factors that determine care needs: (1) the occurrence of complex health problems and (2) the availability of resources to manage health and care (e.g., health literacy, social support). Methods: Estimations of the occurrence of complex health problems and the availability of resources for 2020 were based on registry data and patient-reported data. Estimations for 2040 were based on (a) expected demographic developments, and (b) expert opinions using a two-stage Delphi study with 26 experts from policy making, practice and research in the field of health and social care. Results: The proportion of people aged 65+ with complex health problems and limited resources is expected to increase from 10% in 2020 to 12% in 2040 based on demographic developments, and to 22% in 2040 based on expert opinions. There was high consensus (>80%) that the proportion with complex health problems would be greater in 2040, and lower consensus (50%) on an increase of the proportion of those with limited resources. Developments that are expected to drive the future changes refer to changes in multimorbidity and in psychosocial status (e.g., more loneliness). Conclusion: The expected increased proportion of people aged 65+ with complex health problems and limited resources together with the expected health and social care workforce shortages represent large challenges for public health and social care policy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".