Organization and client perspectives on homelessness in Boston, MA during the COVID-19 Pandemic: A descriptive qualitative study
Bibliographic record
Abstract
The Coronavirus Disease 2019 (COVID-19) pandemic has placed a magnifying glass on what we have been seeing for a long time. With large-scale impacts across the globe, an increased burden has been placed on people experiencing homelessness who already face barriers to necessities such as housing, food, and quality health care. This qualitative descriptive study explored the experiences of people experiencing homelessness during the COVID-19 pandemic and of those working for supporting organizations in the Greater Boston Area between July-November 2020. Additionally, the study identified key recommendations for policymakers or service providers to use in creating inclusive policies and emergency response plans that consider the needs of the homeless community. The study comprised individual interviews and focus group discussions from two groups: 1) employees from supporting organizations (medical organizations, shelters, universities, and housing organizations) and 2) people experiencing homelessness. An inductive content analysis of interview transcripts was conducted to identify emerging themes and recommendations. Community dialogues were held with participants to confirm results and explore any new potential topics or recommendations. Four key themes were identified from interviews: 1) Social inclusion, 2) Services and resources, 3) Community support and collaboration, and 4) Government response and policy. Overall, the COVID-19 pandemic has highlighted major gaps in existing support for people experiencing homelessness on the state and federal levels while also emphasizing the importance of community support and collaboration for this vulnerable population. Further work must be conducted to develop and implement more inclusive policies and regulations to support this community.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".