Bibliographic record
Abstract
LEADER ❙J ada Renee Louis from Virginia had type 1 diabetes.She was 24 years old when she died in 2019, a week after hospital care for diabetic ketoacidosis.'Louis, who did not have health insurance coverage, couldn't afford the cost of her insulin doses and pay her rent.She chose to skip doses in order to pay her rent.' 1 In an online survey by the American Diabetes Association, among 3539 respondents with diabetes, 32.4% had experienced missed payments on bills or cut down spending since the start of the COVID pandemic.Default or imminent default on mortgages or other debts affected 15.6%, twice the national rate, and 7.6% had become temporarily or fully homeless, 48 times the national average. 2orldwide, 1.6 billion people live in inadequate housing and about 15 million are evicted by force annually. 3'On a single night in 2022, roughly 582,500 people were experiencing homelessness in the United States.Six in ten (60%) were staying in sheltered locations…and four in ten (40%) were in unsheltered locations such as on the street, in abandoned buildings, or in other places not suitable for human habitation.' 4 In England, between January and March 2023, 83,240 households were homeless or threatened with homelessness, 5.7% more than the same quarter in 2022.In addition, 37,890 were threatened with homelessness. 5Homeless people may be 'street homeless', for example sleeping rough in doorways, or 'hidden homeless' -sofa surfing or staying in hostels or shelters. 6n England, since 2018, specified public bodies must 'refer, with consent, users of their service who they think may be homeless or threatened with homelessness to a local housing authority of the individual's choice.'The local authority must provide temporary accommodation to an applicant who is unintentionally homeless, eligible for assistance and has priority need.Priority needs include people who are vulnerable, or have a disability.'104,510 households were in temporary accommodation on 31 March 2023…up 10.0% from the same time last year.' 5 Homeless people can also claim benefits (universal credit) if they are on the streets, sofa surfing or staying in a hostel.They need an address to do this -the hostel, day centre, local job centre.People with diabetes may be able to claim disability benefits.(See Box 1.)Factors contributing to homelessness include the person's or household's financial, psychological, cognitive, health, physical, and behavioural state.Geography, availability of affordable, accessible, or appropriate housing, local or national systems or barriers, and discrimination may contribute, as may natural disasters.Homeless people often have multiple health issues including chronic diseases, mental health disorders, LEADER ❙
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.047 | 0.005 |
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".