A Discrete Choice Experiment of Older Self-Funders’ Preferences When Navigating Community Social Care
Bibliographic record
Abstract
Most long-term care systems (social care) for older people rely on some means testing, copayment system, private insurance, or other non-governmental funding to supplement state provision. In England, an estimated quarter of homecare delivery is funded privately. For many older people, the absence of state funding for their care is only part of the problem: they are also expected to search for care in a market characterised by complexity, plurality, and imperfect information. Surprisingly, there are few services available to support private funders to navigate the system. This paper examines willingness to pay for care navigation and seeks to classify heterogeneity of preferences for navigation support. A discrete choice experiment (DCE) survey was completed by 182 participants across England in 2020-21. The results of the random parameter logit model used to analyse preferences showed that people valued information about care options (quality, information, and finances), but they also wanted help to “think things through,” as processing information could be challenging. Generally, participants valued what the navigation service provided, more than how the services were organised and delivered. The study also used latent class analysis to identify four groups with similar preferences, with almost half of participants (48%) expressing high willingness to pay for a comprehensive navigation service. The other three classes represented those with preferences focused on a narrower set of attributes: fast access to information (20%), affordable help to “think things through” (18%), and information provided by their local council (14%). The study demonstrates the potential demand and likely take-up of navigation support if made available to people who pay privately for care. Future research needs to examine the barriers to market development for social care navigation services.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".