Australian and Canadian financial wellbeing policy landscape during COVID-19: An equity-informed policy scan
Bibliographic record
Abstract
Background: This targeted and comprehensive policy scan examined how different levels of governments in Australia and Canada responded to the financial crisis brought on by the COVID-19 pandemic. We mapped the types of early policy responses addressing financial strain and promoting financial wellbeing. We also examined their equity considerations. Methods: Through a systematic search, snowballing, and manual search, we identified Canadian and Australian policies at all government levels related to financial strain or financial wellbeing enacted or amended in 2019-2020. Using a deductive-inductive approach, policies were categorized by jurisdiction level, focal areas, and target population groups. Results: In total, 213 and 97 policies in Canada and Australia, respectively, were included. Comparisons between Canadian and Australian policies indicated a more diversified and equity-targeted policy landscape in Canada. In both countries, most policies focused on individual and family finances, followed by housing and employment areas. Conclusions: The policy scan identified gaps and missed opportunities in the early policies related to financial strain and financial wellbeing. While fast, temporary actions addressed individuals' immediate needs, we recommend governments develop a longer-term action plan to tackle the root causes of financial strain and poor financial wellbeing for better health and non-health crisis preparedness. Statement on Ethics and Informed Consent: This research reported in this paper did not require ethical clearance or patient informed consent as the data sources were published policy documents. This study did not involve data collection with humans (or animals), nor any secondary datasets involving data provided by humans (or from animal studies).
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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 | 0.023 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.043 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".