Digital tools to support technology-enabled budget management in people with acquired brain injury: a rapid review
Bibliographic record
Abstract
People with acquired brain injuries (ABI) face financial challenges that affect their daily lives. Managing finances is a crucial activity that can help avoid social isolation. However, this task becomes difficult for people with ABI because of their cognitive impairments. Recent advances in digital technology can help people with ABI manage their finances more effectively. This study aims to identify and describe available digital tools that can help ABI in budget management, and identify their effectiveness, barriers and facilitators to implementation. To address this issue, we conducted a rapid review of academic databases followed by a modified Google/Google Scholar search to identify the digital tools to support budgeting tasks (DBT) used and tested by people with ABI. Our rapid review included only two articles on the use of DBT. The first study showed that common portable electronic devices were acceptable and desirable as memory and organisational aids for people with ABI. The second study documented the development of a DBT and the perception of users (research participants) who found it appealing and user-friendly. However, for both articles, the technologies used are outdated and lack information on barriers and facilitators to using DBT. In conclusion, this literature review revealed that digital technologies have the potential to support budget management in people with ABI, but technology needs to be made available on the market to benefit the users. Further research and development are needed to create new ways to help people with brain injuries manage their budgets.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".