A rapid review of initiatives to address financial strain and wellbeing in high-income contexts
Bibliographic record
Abstract
INTRODUCTION: The coronavirus disease 2019 (COVID-19) pandemic has exacerbated financial strain among populations worldwide. This is concerning, given the link between financial strain and health. There is little evidence to guide action in this area, particularly from a public health perspective. To address this gap, we examined initiatives to address financial wellbeing and financial strain in high-income contexts. METHODS: We used rapid review methodology and applied an equity-focused lens in our analysis. We searched six databases (MEDLINE, PsycINFO, Web of Science, ProQuest, Informit, and Google Advanced) for peer-reviewed, academic and practice-based literature evaluating initiatives to address financial strain and wellbeing in high-income contexts published between 2015-2020. We conducted a relevancy and quality appraisal of included academic sources. We used EPPI-reviewer software to extract equity-related, descriptive data, and author-reported outcomes. RESULTS: We conducted primary screening on a total of 4779 titles/abstracts (academic n = 4385, practice-based n = 394); of these, we reviewed 182 full text articles (academic n = 87, practice-based n = 95) to assess their relevancy and fit with our research question. A total of 107 sources were excluded based on our selection criteria and relevance to the research question (Figure 1), leaving 75 sources that were extracted for this review (academic n = 39, practice-based n = 36). These sources focused on initiatives predominantly based in Australia, the US, and Canada, with a smaller number from the UK and Europe. Most sources primarily targeted financial literacy and personal/family finances, followed by employment, housing, and education. CONCLUSIONS: We found that holistic initiatives (i.e., complex, wrap-around) that ensured people's basic needs were met (for example, before building financial skills) were aligned with positive and equitable financial wellbeing and financial strain outcomes, as reported in the reviewed studies. We noted significant gaps in the literature related to equity, such as the impact of initiatives on socially excluded populations (e.g., Indigenous peoples, racialised peoples, and rural dwellers). More research using a public health lens is required to guide equitable and sustainable action in this area.
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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 | 0.041 | 0.166 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.042 | 0.037 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".