Instruments for measuring financial well-being among Veterans: A systematic review
Bibliographic record
Abstract
Introduction: Financial health is one of seven aspects that have been identified to appraise the well-being of Veterans. The authors conducted a systematic review to identify instruments that measure financial well-being. Methods: The authors searched the MEDLINE, EMBASE, PsycINFO, AgeLine, PTSD Pubs, Sociological Abstracts, and Social Sciences Abstracts databases from inception to April 13, 2023, for development or validation studies reporting on financial well-being instruments. The consensus-based standards for the selection of health measurement instruments (COSMIN) were used to assess risk of bias and measurement properties of eligible studies. Three Canadian Veteran partners independently reviewed and assessed the clarity and applicability of all identified tools. Results: Thirteen instruments that assessed financial well-being were identified. Of these instruments, five (38%) demonstrated sufficient structural validity, 12 (92%) internal consistency, three (23%) cross-cultural validity, one (8%) test-retest reliability, and 10 (77%) construct validity. Veteran partners identified four instruments as very clear and very applicable to Veterans: Well-Being Inventory (WBI), Economic Quality of Life Measure (Econ-QOL), Personal Financial Wellness Scale (PFWS), and Living Standards Capabilities for Elders Scale (LSCAPE). The WBI was developed and validated with a Veteran sample. The Econ-QOL was developed with a general sample and validated among Veterans. Discussion: Among the four instruments reporting strong psychometric properties and endorsed by the study's Veteran partners, only the WBI was both developed and validated with a U.S. Veteran population, whereas the Econ-QOL was only validated with that population. The PFWS and LSCAPE appear promising but require validation among Veterans.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".