The effectiveness of smartphone apps in improving financial capability
Bibliographic record
Abstract
This study is the first to assess whether smartphone apps can be utilised to improve financially capable behaviours.In this study four smartphone apps, packaged together under the title 'Money Matters', were provided to working age members (16-65 years) of the largest credit union in Northern Ireland (Derry Credit Union).The smartphone apps consisted of a loan interest comparison app, an expenditure comparison app, a cash calendar app, and a debt management app.The assessment methodology used was a Randomised Control Trial (RCT) with the UK Financial Capability Outcome Frameworks used to set the context for the assessment.For those receiving the apps (the treatment group) statistically significant improvements were found in a number of measures designed to gauge 'financial knowledge, understanding and basic skills' and 'attitudes and motivations'.These improvements translated into better financially capable behaviours; those receiving the apps were more likely to keep track of their income and expenditure and proved to be more resilient when faced with a financial shock.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".