Comparing Client and Provider Preferences for HIV Care Coordination Program Features using Discrete Choice Experiments
Bibliographic record
Abstract
ABSTRACT Introduction The New York City (NYC) HIV Care Coordination Program (CCP) is designed to promote care engagement and treatment adherence among the people with HIV (PWH) who struggle the most with these key components of managing their health. We assessed preferences for CCP components among PWH enrolled in the program (“clients”) and among providers of CCP services. In this report we compare the preferences between clients and providers, previously analyzed separately. Methods We used a discrete choice experiment to assess preferences for four CCP features (“attributes”): Help with Adherence to Antiretroviral Therapy (ART), Help with Primary Care Appointments, Help with Issues other than Primary Care, and Where Program Visits Happen. Each of these attributes had three to four variants (“levels”). In the original surveys, levels within Where Program Visits Happen varied by participant type (client versus provider). We re-coded the levels by visit location (VL) or by travel time (TT) to make them comparable, and report results from both approaches. Preferences were quantified using the relative importance of the attributes and utility of the levels. Results January 2020 to March 2021, 152 providers and 181 clients completed the survey. In both the VL and TT analyses, clients were most influenced by Help with Adherence to ART, preferring medication reminders via phone or text, and Where Program Visits Happen, preferring visits via phone or video chat. In the VL analysis, providers were most influenced by Help with Adherence to ART, valuing directly observed therapy most highly, and Help with Issues other than Primary Care, valuing helping clients with connections to specialty medical care. In the TT analysis, providers were most influenced by Where Program Visits Happen, preferring to meet at clients’ homes, and Help with Issues other than Primary Care, again preferring helping clients make connections to specialty medical care. Conclusions Client and provider preferences clearly diverged with regard to CCP service intensity: in the aggregate, clients tended to prefer lower-intensity services, whereas providers endorsed higher-intensity services. These results highlight the importance of engaging clients as partners in decisions about program services so that they are aligned with client values.
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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 | 0.026 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".