Bibliographic record
Abstract
There are complex issues surrounding hospital discharge planning for people experiencing homelessness. The issue involves the disconnection across policy areas of housing, income supports and mental health, and later health generally. Different models for different types of communities (large urban, mid-size, small and rural areas) likely need to be developed as well as for different types of conditions and different housing histories. The quality of data needs improvement including accuracy. Housing items need to be part of admission processes so that the need for post-discharge housing can be quickly flagged and more accurate data can be made available. System improvements need to include all levels of government, people with lived experience, and health as well as housing/homeless sectors. The income support sector also needs to be included. Discharge planning often assumes there is a fixed address after discharge. This clearly misses the needs of people who have lost their housing.
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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.108 | 0.078 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".