Public Health Responses to Homelessness During COVID-19 in Ireland: Implications for Health Reform
Bibliographic record
Abstract
The intersection between health and homelessness is well documented and has been made more explicit since the onset of the coronavirus disease of 2019 (COVID-19). In the Global North, COVID-19 related public health responses targeting homeless populations have varied across countries, including greater national-level financial investment, temporarily housing those in congregate facilities in ‘self-contained’ accommodation, time-limited eviction moratoriums and dedicated health system interventions across homelessness and addiction services to enable social distancing and self-isolation. Yet questions remain about the immediate and longer-term outcomes of these newly implemented responses and whether they will be sustained. Such considerations are critical and timely in countries such as Ireland, where significant health reform is underway. In this paper, we use Ireland as a case study to explore whether and how public health responses to homelessness during COVID-19 hold important insights for the development of more effective health policy. Drawing on publicly available Irish data, we present a secondary analysis of COVID-19 infection rates amongst homelessness service users as well as trends in emergency accommodation usage between 2020 and 2021. Focusing specifically on public health measures implemented during the pandemic via the health and housing systems, respectively, we discuss how such interventions may have impacted on these homelessness figures in both intended and unexpected ways. We conclude by teasing out and unpacking relevant lessons for health reform.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".