Intersectoral interagency partnerships to promote financial capability in older people
Bibliographic record
Abstract
From the second quarter of 2008, the UK economy entered a period of economic decline. Older people are particularly vulnerable during these times. To promote ways in which older people can be better supported to maintain their financial well-being, this study explored the sources older people utilize to keep themselves financially informed. Interviews with older people (n = 28) showed that older people access trusted sources of information (e.g. healthcare professionals) rather than specialist financial information providers (e.g. financial advisors) which highlighted the need for interagency working between financial services in the private, public and voluntary sectors. An example of how such interagency partnerships might be achieved in practice is presented with some recommendations on directions for future research into interagency working that spans public, private and voluntary sectors.
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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 | 0.025 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.037 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".