Housing situation and healthcare for patients in a psychiatric centre in Berlin, Germany: a cross-sectional patient survey
Bibliographic record
Abstract
OBJECTIVE: To determine the housing situation among people seeking psychiatric treatment in relation to morbidity and service utilisation. DESIGN: Cross-sectional patient survey. SETTING: Psychiatric centre with a defined catchment area in Berlin, Germany, March-September 2016. PARTICIPANTS: 540 psychiatric inpatients including day clinics (43.2% of all admitted patients in the study period (n=1251)). MAIN OUTCOME MEASURES: Housing status 30 days prior the interview as well as influencing variables including service use, psychiatric morbidity and sociodemographic variables. RESULTS: In our survey, 327 participants (68.7%) currently rented or owned an own apartment; 62 (13.0%) reported to be homeless (living on the street or in shelters for homeless or refugees); 87 (18.3%) were accommodated in sociotherapeutic facilities. Participants without an own apartment were more likely to be male and younger and to have a lower level of education. Homeless participants were diagnosed with a substance use disorder significantly more often (74.2%). Psychotic disorders were the highest among homeless participants (29.0%). Concerning service use, we did neither find a lower utilisation of ambulatory services nor a higher utilisation of hospital-based care among homeless participants. CONCLUSIONS: Our findings underline the need for effective housing for people with mental illness. Despite many sociotherapeutic facilities, a concerning number of people with mental illness is living in homelessness. Especially early interventions addressing substance use might prevent future homelessness.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".