Assessing the Needs of Shelter Providers
Bibliographic record
Abstract
Homelessness is an unfortunate societal problem, with some estimates suggesting that there are over a quarter million homeless individuals in the United States (Annual Homeless Assessment Report to Congress, 2021). Consequences of homelessness are wide ranging and can include the development or increase in mental health symptoms and even a significant decrease in life expectancy. A significant amount of research has been conducted on the issue of homelessness in the United States. Much of the research attempts to discover specific factors that are associated with homelessness, such as substance use, poor family support, mental illness, etc. Research has also been conducted on the effectiveness of programs that are designed to curb homelessness. The majority of the previous research focuses primarily on describing the programs and identifying factors associated with failure to complete programming, which in turn results in future episodes of homelessness. Little attention has been paid to identifying specific factors that homeless programs themselves view as deficits in their ability to provide adequate services. The purpose of this study was to complete a needs assessment with homeless shelter providers in the Midwest, in order to examine their perceptions of programming in the following domains: housing, employment, mental health services, substance abuse and general funding. Due to the overrepresentation of veterans among the homeless, services for this population were also assessed. Respondents described a lack of overall funding for homeless services, as well as funding deficits in most of the specific programming domains. Deficits were most pronounced for mental health and substance abuse programming. Implications of these findings as well as limitations and suggestions for future research are detailed.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".