Patient engagement in the development of HIV-specific health instruments: a systematic mixed studies review using Thematic Analysis
Bibliographic record
Abstract
Background: Since the initial spread of HIV infection in the 1980s and the subsequent epidemic, people living with HIV have sought involvement in and helped define the fight against HIV/AIDS.This grassroots HIV activism markedly contributed to the development of patient engagement research methods.Involving patients in research through patient engagement may offer a diverse range of benefits, and is actively supported by funding agencies and governmental bodies.However, there have been few rigorous investigations of the conduct of patient engagement.It is unclear how patient engagement is carried out and described in published HIV health research.Objectives: To synthesize current evidence about the role and results of patient engagement in the development of HIV health measures as reported in the scientific literature.Methods: This is a mixed studies systematic review that covers scholarly publications from 1993 to 2015.Search for literature describing HIV-specific instrument development was conducted in the following databases: Pubmed, Medline, PsychINFO, Health and Psychosocial Instruments, and Embase.Quality appraisal was conducted using the Mixed Methods Appraisal Tool.Then, thematic analysis was performed to meaningfully synthesize knowledge generated about the topic under investigation.Results: Our queries generated 4363 records; after screening and verifying eligibility, 39 records were retained for analysis.The quality appraisal highlights poor reporting of engagement
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 | 0.122 | 0.221 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.038 | 0.031 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".