Implementing Web-Based Interventions in HIV Primary Care Clinics: Pilot Implementation Evaluation of Positive Health Check
Bibliographic record
Abstract
BACKGROUND: Web-based interventions can help people living with HIV achieve better clinical outcomes and behaviors, but integrating them into clinical practice remains challenging. There is a gap in understanding the feasibility of implementing these interventions in HIV clinic settings from the clinicians' perspective. OBJECTIVE: The goal of the research was to determine whether Positive Health Check (PHC)-a Web-based, tailored video counseling tool focused on increasing patient adherence and retention in care and reducing HIV risk among HIV-positive patients-was acceptable, appropriate, and feasible for HIV primary care clinic staff to implement in clinic workflows. METHODS: A multiple-case study design was used to evaluate the pilot implementation. Four primary care clinics located in the southeastern United States implemented PHC over a 1-month period. Nine clinic staff across the clinics participated in structured interviews before, during, and after the implementation. In total, 54 interviews were conducted. We used a framework analysis approach to code the data and identify themes related to implementation outcomes, including acceptability, appropriateness, and feasibility. We also analyzed patient intervention use metrics (n=104) to quantify patient intervention completion rates (n=68). RESULTS: Overall, clinicians viewed PHC as acceptable and appropriate. Themes that emerged related to these implementation outcomes include the ability for PHC to increase provider-patient communication and its ability to engage patients due to the tailored and interactive design. While generally feasible to implement, challenges to the clinic workflow and physical environment were areas that clinics needed to manage to make PHC work in their clinics. CONCLUSIONS: Findings from this pilot implementation suggest that clinical staff viewed PHC as acceptable and appropriate, especially as more patients used the intervention over the pilot period. Feasibility of implementation was challenging in some cases, and lessons learned from this pilot implementation can provide information for larger scale tests of the intervention that include assessment of both implementation outcomes and clinical outcomes.
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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".