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Record W6923570675 · doi:10.14288/1.0442093

Characterization of depression, suicidal ideation, and anxiety among adults who are homeless or precariously housed

2024· article· en· W6923570675 on OpenAlexaboutno aff

Bibliographic record

VenuecIRcle (University of British Columbia) · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsAnxietyContext (archaeology)Suicidal ideationLongitudinal studyDepression (economics)Beck Anxiety InventoryObservational studyBeck Depression Inventory

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.966
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.012
GPT teacher head0.249
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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