Financial Capability Interventions Used for Specific Diagnoses Related to Functional Impairment: A Scoping Review
Bibliographic record
Abstract
IMPORTANCE: Occupational therapists often address financial occupations of clients with acquired functional impairments who experience challenges with financial capability (FC). OBJECTIVE: To explore the intervention literature aimed at improving FC in five diagnostic adult populations. DATA SOURCES: MEDLINE, CINAHL, PsycInfo, EconLit, and EMBASE; researchers also completed backward and forward citation searching and contacted expert authors. STUDY SELECTION AND DATA COLLECTION: Two independent reviewers completed article screening, selection, and extraction using a scoping review approach; a priori inclusion criteria were peer-reviewed articles, written in English, involving adults with one of five diagnostic conditions, describing any intervention to improve FC. FINDINGS: Twenty-four articles met the inclusion criteria. Most articles were aimed at substance use or mental health populations (n = 20); fewer focused on brain injury (n = 2), multiple sclerosis (n = 1), or mixed-diagnosis (n = 1) populations. Only 4 were randomized controlled trials (RCTs). Interventions were heterogeneous and complex, including components of skills training (n = 21), individualized budgeting (n = 18), representative payeeship (n = 11), education (n = 10), structured goal setting (n = 7), savings building (n = 5), metacognitive strategies (n = 2), and assistive technology (n = 1). CONCLUSIONS AND RELEVANCE: Despite growth in the area, the literature regarding FC intervention is limited, with few RCTs and many populations unrepresented. The literature for a systematic review of FC intervention efficacy for these populations is insufficient, particularly because included studies used varied components, limiting comparison. Further research is imperative to guide evidence-based practice. Plain-Language Summary: This study is an overview of literature about interventions to address the financial occupations of clients with acquired functional impairments. The findings give occupational therapy researchers and clinicians the information they need to begin analyzing, using, and building the evidence to support the use of interventions to improve clients' financial capability and well-being.
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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 | 0.021 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| 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".