Untangling Concepts of Financial Circumstances for Public Health Professionals and Scholars: A Glossary and Concept Map
Bibliographic record
Abstract
Public health discipline and practice have prioritized work on poverty and populations at high risk for material deprivation, with less consideration for the full spectrum of financial circumstances relative to well-being. Public health can make a much-needed contribution to this area, which is currently dominated by the financial industry, focused on individual behaviors, and lacking the definitional consensus needed for research and evaluation. A population-level lens can reveal the social determinants and health consequences of real or perceived poor financial circumstances. This article aims to improve conceptual understanding of financial circumstances among public health scholars and professionals. We identified concepts through a critical literature review of peer-reviewed and practice-based resources on financial well-being and financial strain. We developed a glossary of concepts related to financial circumstances and categorized concepts according to their level of influence using an approach informed by socioecological models. We provide a concept map that illustrates the relationships between concepts in the context of their levels of influence. This article will help to advance an agenda on financial well-being promotion in public health research and practice. (Am J Public Health. 2024;114(1):79–89. https://doi.org/10.2105/AJPH.2023.307449 )
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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 | 0.019 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.027 | 0.021 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| 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".