Understanding Older Adults' Long-term Financial Practices
Bibliographic record
Abstract
As older adults (OAs) approach retirement, their financial management requirements change as they shift from income to pension or other assets. However, existing interactive budgeting apps neither support this transition, nor facilitate long-term financial planning. Our research aims to understand OAs' technological, educational, and behavioural barriers toward the adoption of budgeting applications. It also aims to uncover the design requirements for long-term financial planning apps that would overcome these adoption barriers. For this, we conducted a contextual inquiry to understand seniors' financial management practices. In-depth qualitative data collected both from individual sessions and participatory design activities has revealed significant gaps between the capabilities of existing apps; the best practices around long-term planning; and the attitudes and behaviours of OAs. We present here an argument, based on the preliminary analysis of the field data, for approaching the design of senior-centred interactive budgeting apps from a behavioural change and educational perspective.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; both teacher heads agree on what is shown here.
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".