Memory impairment among people who are homeless: A systematic review
Bibliographic record
Abstract
Cognitive impairment may interfere with an individual's ability to function independently in the community and may increase the risk of becoming and remaining homeless. The purpose of this study was to systematically review the literature on memory deficits among people who are homeless in order to gain a better understanding of its nature, causes and prevalence. Studies that measured memory functioning as an outcome among a sample of homeless persons were included. Data on sampling, outcome measures, facet of memory explored and prevalence of memory impairment were extracted from all selected research studies. Included studies were evaluated using a critical appraisal process targetted for reviewing prevalence studies. Eleven studies were included in the review. Verbal memory was the most commonly studied facet of memory. Potential contributing factors to memory deficits among persons who are homeless were explored in seven studies. Memory deficits were common among the samples of homeless persons studied. However, there was a great deal of variation in the methodology and quality of the included studies. Conceptualisations of "homelessness" also differed across studies. There is a need for more controlled research using validated neuropsychological tools to evaluate memory impairment among people who are homeless.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".