Creating Productive Tensions: Clinicians Working with Patients as Peer Researchers in a Community-Based Participatory Research Study of the Lived Experience of HIV-Associated Neurocognitive Disorder (HAND)
Bibliographic record
Abstract
About 50% of people living with HIV will develop HIV-associated neurocognitive disorder (HAND) during their lifetime, and we know that cognitive issues are a concern for people living with HIV. However, limited information exists regarding how HAND is managed and coped with, or how cognitive issues are discussed with others, including health care professionals. Following a community-based research approach, we conducted 25 interviews in 2016 aimed to (1) build the capacity of people living with HIV, (2) facilitate participant recruitment and data collection, (3) increase the validity and reliability of our data analysis results, and (4) facilitate knowledge transfer and exchange regarding HAND. After thorough training, we engaged a number of peer researchers living with HAND in the analysis and knowledge transfer and exchange (KTE) phases of the study. This engagement prompted a number of tensions between the clinicians and the peers that we learned to navigate and make productive. We conclude that it is possible to engage patients and providers only if careful attention, time and human resources are provided to navigating the emerging tensions. The outcomes of our study suggest that engaging an interdisciplinary team across multiple sites with PRAs is a valuable method for comprehensively exploring the lived experience of a complex chronic condition such as HAND.
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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 | 0.081 | 0.142 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.042 | 0.032 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.007 | 0.027 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".