A Policy-Ready Public Health Guidebook of Strategies and Indicators to Promote Financial Well-Being and Address Financial Strain in Response to COVID-19
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic has adversely affected the financial well-being of populations globally, escalating concerns about links with health care and overall well-being. Governments and organizations need to act quickly to protect population health relative to exacerbated financial strain. However, limited practice- and policy-relevant resources are available to guide action, particularly from a public health perspective, that is, targeting equity, social determinants of health, and health-in-all policies. Our study aimed to create a public health guidebook of strategies and indicators for multisectoral action on financial well-being and financial strain by decision makers in high-income contexts. METHODS: We used a multimethod approach to create the guidebook. We conducted a targeted review of existing theoretical and conceptual work on financial well-being and strain. By using rapid review methodology informed by principles of realist review, we collected data from academic and practice-based sources evaluating financial well-being or financial strain initiatives. We performed a critical review of these sources. We engaged our research-practice team and government and nongovernment partners and participants in Canada and Australia for guidance to strengthen the tool for policy and practice. RESULTS: The guidebook presents 62 targets, 140 evidence-informed strategies, and a sample of process and outcome indicators. CONCLUSION: The guidebook supports action on the root causes of poor financial well-being and financial strain. It addresses a gap in the academic literature around relevant public health strategies to promote financial well-being and reduce financial strain. Community organizations, nonprofit organizations, and governments in high-income countries can use the guidebook to direct initiative design, implementation, and assessment.
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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 | 0.027 | 0.085 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".