The Generative Mechanisms of Financial Strain and Financial Well-Being: A Critical Realist Analysis of Ideology and Difference
Bibliographic record
Abstract
BACKGROUND: Rapid, strategic action is required to mitigate the negative and unequal impact of the coronavirus disease 2019 (COVID-19) pandemic on the financial well-being (FWB) of global populations. Personal financial strain (FS) worsened most significantly among systematically excluded groups. Targeted government- and community-led initiatives are needed to address these inequities. The purpose of this applied research was to identify what works for whom, under what conditions, and why in relation to community and government initiatives that promote personal and household FWB and/or address FS in high income economies. METHODS: We employed a critical realist analysis to literature that reported on FWB/FS initiatives in high income countries. This included initiatives introduced in response to the pandemic as well as those that began prior to the pandemic. We included sources based on a rapid review. We coded academic, published literature (n=39) and practice-based (n=36) reports abductively to uncover generative mechanisms - ie, underlying, foundational factors related to community or government initiatives that either constrained and/or enabled FWB and FS. RESULTS: We identified two generative mechanisms: (1) neoliberal ideology; and (2) social equity ideology. A third mechanism, social location (eg, characteristics of identity, location of residence), cut across the two ideologies and demonstrated for whom the initiatives worked (or did not) in what circumstances. Neoliberal ideology (ie, individual responsibility) dominated initiative designs, which limited the positive impact on FS. This was particularly true for people who occupied systematically excluded social locations (eg, low-income young mothers). Social equity-based initiatives were less common within the literature, yet mostly had a positive impact on FWB and produced equitable outcomes. CONCLUSION: Equity-centric initiatives are required to improve FWB and reduce FS among systemically excluded and marginalized groups. These findings are of relevance now as nations strive for financial recovery in the face of the ongoing global pandemic.
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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 | 0.029 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.006 | 0.070 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".