Engaging patients in the HIV care continuum through referral-making behaviours and patterns: A descriptive cross-sectional study
Bibliographic record
Abstract
Introduction: HIV continuum of care consists of five steps needed to effectively treat and prevent the spread of HIV. Linkage to and retention of patients to this Continuum of Care is a global priority. However, the COVID-19 pandemic has impacted the quality of this Continuum, as people living with HIV, have had to shelter reducing their access to services. As well, HIV agencies have had to close, reduce hours, and shift personnel. Purpose and Methods: The purpose of this descriptive cross-sectional study was to examine the person-centered referral-making behaviors and patterns used by providers to engage patients in the care continuum. Three classes of linkage behaviors among 285 providers in 34 community agencies in New York City were identified using latent class analysis. Results: These linkage behaviors include High (48%); Moderate (34%); and Low (18%). Both High and Moderate consisted of a blend of active and passive strategies and tracking systems. The High included more active strategies such as escorting patients to appointments. Linkage class membership was significantly associated with frequency of linkages to primary care (p=.020). COVID-19 disruptions demonstrate how the Care Continuum has been undermined by insufficient organizational resources. Conclusion: Findings suggest, addresses gaps in linkages should enhance the overall Continuum of Care provided to individuals diagnosed and living with HIV.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".