Characterization of depression, suicidal ideation, and anxiety among adults who are homeless or precariously housed
Bibliographic record
Abstract
Depression, suicidal ideation (SI), and anxiety are common in people who are homeless or precariously housed. However, predictors of these conditions over time in these individuals remain understudied, and this information is crucial for the design of effective support services. The objective of this thesis was to determine longitudinal predictors of depressive symptom severity, SI, and anxiety symptom severity among adults who are homeless or precariously housed in a community-based setting. Data were collected as part of the “Hotel Study”, an observational study of people living in an impoverished neighbourhood in Vancouver, Canada. Participants (N=475) completed assessments of health and social factors monthly over a period of three years. SI and anxiety symptoms were assessed by the Maudsley Addiction Profile, which has demonstrated reliability in people who are homeless. The Beck Depression Inventory (BDI) assessed depressive symptoms, but its suitability for the present context was unknown because it was developed in a clinical setting. Thus, we first determined the psychometric properties of the BDI. Using the measurement approaches of Classical Test Theory and Rasch analysis, we found the BDI to be valid and reliable for assessing depressive symptom severity in the group. Next, we identified longitudinal predictors of depressive symptom severity, SI, and anxiety symptom severity using mixed effects regression models. We found that having a history of clinical depression, more trauma, more anxiety and psychotic symptoms, and frequent non-prescribed opioid use were associated with more depressive symptoms. Predictors of SI included having a history of psychotic disorder, more trauma, more anxiety symptoms, and concurrent clinical depression. Regarding anxiety, we found that having a history of anxiety disorder, history of alcohol dependence, more depressive and psychotic symptoms, daily tobacco use, frequent anxiolytic use, and recent negative social interactions were associated with increased symptoms. The results suggest that managing psychotic symptoms, substance use, and the lasting effects of trauma may help alleviate depressive and anxiety symptoms in adults who are homeless or precariously housed. Given the variety of predictors identified, decreasing the burden of these conditions on those affected likely requires multidisciplinary interventions at the community, clinical and policy levels.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".