Promoting Emergency Response for Homeless Service Agencies: Field-Based Recommendations from Two Municipalities in Nova Scotia, Canada
Bibliographic record
Abstract
The fast unfolding of the global COVID-19 pandemic has disproportionately affected the homeless sector by triggering tremendous challenges for individuals experiencing homelessness (IEHs) and related service agencies. This quick-response research project qualitatively collected time-sensitive data from the IEHs and service stakeholders (SSs) experiences, challenges, efforts, and suggestions during the first wave of COVID-19 in the two most populated municipalities in the province of Nova Scotia, Canada, namely, Halifax Regional Municipality and Cape Breton Regional Municipality. Through analyzing and synthesizing the standpoints from both IEHs and SSs, this technical note presents recommendations, addressing the practical challenges that IEHs have been confronting during COVID-19 and systemic issues in which homelessness is rooted. These recommendations will assist community-based agencies in improving their emergency response capacity, better serving IEHs in COVID-19 in particular, and supporting other vulnerable and marginalized populations in future extreme events in general.
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".