Scaling Up Information Sharing on HIV-Associated Neurocognitive Disorder: Raising Awareness and Knowledge Among Key Stakeholders
Bibliographic record
Abstract
Although the majority of specialists and researchers in the field of HIV/AIDS are aware and knowledgeable about HIV-associated neurocognitive disorder (HAND) as a condition that affects as much as 50% of people living with HIV/AIDS (PLWH), research has documented that many health care and service providers who work directly with PLWH are either unaware of HAND or believe they do not know enough information about HAND to effectively support their clients experiencing neurocognitive challenges. Based on the findings of a qualitative study that interviewed 33 health care and service providers in HIV/AIDS services to identify and examine their awareness and knowledge on HAND, this article argues for utilizing a combination of Public Health Informatics principles; communication techniques, propagation strategies, and recognized approaches from Implementation and Dissemination Science; and social media and online discussion platforms, in addition to traditional Knowledge Mobilization strategies, to scale up information sharing on HAND among all relevant stakeholders. Increasing information sharing among stakeholders would be an important step to raising awareness and knowledge on HAND, and consequently, improving care, services, and support for PLWH and neurocognitive issues.
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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 | 0.035 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| 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".