Down and Out in London: Addictive Behaviors in Homelessness
Bibliographic record
Abstract
Backgrounds and aims Problem gambling occurs at higher levels in the homeless than the general population. Past work has not established the extent to which problem gambling is a cause or consequence of homelessness. This study sought to replicate recent observations of elevated rates of problem gambling in a British homeless sample, and extend that finding by characterizing (a) the temporal sequencing of the effect, (b) relationships with drug and alcohol misuse, and (c) awareness and access of treatment services for gambling by the homeless. Methods We recruited 72 participants from homeless centers in Westminster, London, and used the Problem Gambling Severity Index to assess gambling involvement, as well as DSM-IV criteria for substance and alcohol use disorders. A life-events scale was administered to establish the temporal ordering of problem gambling and homelessness. Results Problem gambling was evident in 23.6% of the sample. In participants who endorsed any gambling symptomatology, the majority were categorized as problem gamblers. Within those problem gamblers, 82.4% indicated that gambling preceded their homelessness. Participants displayed high rates of substance (31.9%) and alcohol dependence (23.6%); these were not correlated with PGSI scores. Awareness of treatment for gambling was significantly lower than for substance and alcohol use disorders, and actual access of gambling support was minimal. Discussion and conclusions Problem gambling is an under-recognized health issue in the homeless. Our observation that gambling typically precedes homelessness strengthens its role as a causal factor. Despite the elevated prevalence rates, awareness and utilization of gambling support opportunities were low compared with services for substance use disorders.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".