Evolution of an International Research Collaborative in HIV and Rehabilitation: Community Engaged Process, Lessons Learned, and Recommendations
Bibliographic record
Abstract
BACKGROUND: Human immunodeficiency virus (HIV) is increasingly considered a chronic illness. Rehabilitation can address some of the health challenges of people living with HIV (PLWHIV); however, the field is emerging. OBJECTIVES: We describe our experience establishing an international collaborative in HIV and rehabilitation research using a community engaged approach. METHODS: The Canada-UK (now Canada-International) HIV and Rehabilitation Research Collaborative (CIHRRC) is a network of more than 85 PLWHIV, researchers, clinicians, and representatives from community-based organizations collectively working to advance knowledge on HIV and rehabilitation. RESULTS: Activities and outcomes include facilitating knowledge transfer and exchange (KTE), establishing and strengthening multistakeholder partnerships, and identifying new and emerging priorities in the field. Collaboration and support from community organizations fostered mechanisms to raise the profile of, and evidence for, rehabilitation in the context of HIV. Considerations of scope, partnership, and sustainability are important. We offer recommendations for developing an international community-academic-clinical research collaborative using a community-engaged approach. CONCLUSIONS: Research networks involving community-academic-clinical partnerships can help to promote KTE and establish a coordinated response for addressing priorities in an emerging field.
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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 | 0.267 | 0.180 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.024 | 0.024 |
| Scholarly communication | 0.035 | 0.043 |
| Open science | 0.011 | 0.040 |
| Research integrity | 0.017 | 0.027 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".