Examining the needs of persons experiencing homelessness: Bringing the voice of lived experience to policy priorities
Bibliographic record
Abstract
Policy related to homelessness varies across federal, state, and local levels influencing access to health and social services among persons experiencing homelessness. With a diversity of community stakeholders comes a diversity of ideas about which health and social services to provide and prioritize. Despite the unique insights PEH are able to offer on their own situations, their voices often remain excluded from research and policy. The aim of the current study was to identify the basic needs and health and social service priorities of PEH, to incorporate these voices into policy decisions and prioritization. Fifteen participants age 18 and older who had any experience of homelessness in Salt Lake Countyparticipated in in-depth qualitative interviews between February and March 2020. Open-ended questions enabled the interviews to naturally progress and focus on what was most relevant to participants and their experience. Data were categorized into the services and supports identified as the most needed by PEH and to help lift people out of homelessness: 1) Housing and shelter, 2) Income and employment, 3) Transportation, 4) Food, 5) Physical and mental health services, and 6) Social support. We describe the needs, challenges, and solutions as identified and narrated by study participants. Findings offer critical insight into how the homelessness crisis should be approached by policymakers as the ways in which PEH understand and utilize the system of available resources and supports must align with their abilities and realities. Without these in-depth discussions with PEH about how they understand and meet their basic needs, there will be ongoing gaps in service delivery and mismatches in the attempts made to serve this population.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".