Differential impacts of <scp>COVID</scp> ‐19 and associated responses on the health, social well‐being and food security of users of supportive social and health programs during the <scp>COVID</scp> ‐19 pandemic: A qualitative study
Bibliographic record
Abstract
The effects of the coronavirus disease-2019 (COVID-19) pandemic on the lives of underserved populations are underexplored. This study aimed to identify the impacts of the COVID-19 pandemic and associated public health responses on the health and social well-being, and food security of users of Housing First (HF) services in Toronto (Canada) during the first wave of the COVID-19 pandemic. This qualitative descriptive study was conducted from July to October 2020 in a subsample of 20 adults with a history of homelessness and serious mental disorders who were receiving HF services in Toronto. A semi-structured interview guide was used to collect narrative data regarding health and social well-being, food security and access to health, social and preventive services. A thematic analysis framework guided analyses and interpretation of the data. The COVID-19 pandemic and response measures had a variable impact on the health, social well-being and food security of participants. Around 40% of participants were minimally impacted by the COVID-19 pandemic. Conversely, among the remaining participants (impacted group), some experienced onset of new mental health problems (anxiety, stress, paranoia) or exacerbation of pre-existing mental disorders (depression, post-traumatic stress disorder and obsessive-compulsive disorder). They also struggled with isolation and loneliness and had limited leisure activities and access to food goods. The pandemic also contributed to disparities in accessing and receiving healthcare services and treatment continuity for non-COVID-19 health issues for the negatively impacted participants. Overall, most participants were able to adhere to COVID-19 public health measures and get reliable information on COVID-19 preventive measures facilitated by having access to the phone, internet and media devices and services. In conclusion, the COVID-19 pandemic and associated response measures impacted the health, social well-being, leisure and food security of people with experiences of homelessness and mental disorders who use supportive social and housing services in diverse ways.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".