PTSD Symptom Severity and Overall Quality of Life Pre-and During COVID-19 Among Adults Seeking Housing Support Services
Bibliographic record
Abstract
COVID-19 stay-at-home orders have had far-reaching negative consequences on mental health and quality of life (QOL). This is especially true for vulnerable populations, such as those who are unhoused, as they are more likely than the general population to have been struggling with a mental illness and poor QOL well before the pandemic. This exploratory cross-sectional study explored differences in PTSD symptom severity and overall QOL among a cohort of adults who were seeking housing support services pre-COVID-19 (n = 226 compared to a cohort of adults who were seeking housing support services during COVID-19 (n =205). All data were collected upon enrollment into a permanent supportive housing program. Participants seeking housing support services during COVID-19 compared to pre-COVID-19 were significantly more likely to report higher PTSD symptoms (t=3.14, p=0.001) and poorer QOL (=9.81, p=0.001), however differences were no longer observed at the five percent significance level once several covariates were controlled for in the analysis. Despite the lack of statistical significance at the multivariate level (which is likely a result of challenges with the data and the level of statistical significance selected and not a reflection of true differences between the cohorts), the clinical significance of the findings has implications for planning behavioral health services for unhoused individuals seeking housing support services, especially as we exit the pandemic.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".