Protocol for a systematic review of treatment adherence for HIV, hepatitis C and tuberculosis among homeless populations
Bibliographic record
Abstract
BACKGROUND: Homelessness is a global issue and HIV, hepatitis C and tuberculosis are known to be prevalent in this group. Homeless populations face significant barriers to care. We aim to summarise evidence of treatment initiation and completion for homeless populations with these infections, and their associated factors, through a systematic review and meta-analysis. METHODS: We will search MEDLINE, Embase and CINAHL for all study types and conference abstracts looking at either (1) treatment initiation in a cohort experiencing homelessness with at least one of HIV, hepatitis C, active tuberculosis and/or latent tuberculosis infection (LTBI); (2) treatment completion for those who initiated treatment. We will perform a meta-analysis of the proportion of those with each infection who initiate and complete treatment, as well as analysis of individual and health system factors that may affect adherence levels. We will evaluate the quality of research papers using the Newcastle-Ottawa scale. DISCUSSION: Given the political emphasis on global elimination of these diseases, and the current lack of understanding of effective and equitable treatment adherence strategies in homeless populations, this review will provide insight to policy-makers and service providers aiming to improve homeless healthcare. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42019153150.
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 | 0.089 | 0.112 |
| Meta-epidemiology (narrow) | 0.008 | 0.007 |
| Meta-epidemiology (broad) | 0.024 | 0.023 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.101 | 0.014 |
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".