Developing an Estimate of Supported Housing Needs for Persons with Serious Mental Illnesses
Bibliographic record
Abstract
A rich body of literature attests to the importance of affordable accommodation and support services necessary, appropriate, and acceptable to persons disabled by a mental illness. However, there is a little which provides a means for housing and service planners to determine the gap between available supportive housing and need. Such understandings are needed to prepare strategies and develop the resources needed to accommodate persons with a disabling mental illness in the community. While housing studies that examine shelter needs of the homeless acknowledge that a sizable proportion has a disabling mental illness, these numbers underestimate need in the cohort that experiences disabling mental illnesses. This underestimate exists because many of those who are disabled by mental illness and in need of supportive housing are among the hidden homeless: doubled-up, couch-surfing, and temporarily sheltered by friends and family. Thus, little is known about the size of this cohort or their supportive shelter needs. The present analysis examines two approaches and offers one methodology as most feasible and parsimonious which can approximate housing need and may be extrapolated to other urban locations.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".