Is there evidence for brain health as a unified construct relevant to aging with HIV?
Bibliographic record
Abstract
Neuropsychiatric disorders and mental illnesses significantly impact the lives of individuals and families and are among the leading causes of years of life lost to disability (DALYs), globally, emphasizing the importance of understanding and maintaining brain health. Brain health is a multi- dimensional construct reflecting the brain's role in cognition, mood, emotional stability, motivation, and energy (and of course movement). Given the high prevalence of sub-optimal brain health in individuals with HIV, it is crucial to develop methods to define and measure brain health as a unified construct relevant to aging with HIV.Common psychometric and statistical models struggle to estimate intricate constructs due to their unpredictability, complexity, and instability. Network analysis (NA) research emerged in the 1980s, combining sociology, anthropology, and psychology disciplines to understand social phenomena. NA captures the complex relationships between observables, allowing for a comprehensive understanding of brain health.The objectives of this thesis are twofold: (1) to provide evidence that brain health as operationalized for the Positive Brain Health Now (BHN) cohort is a unified construct; and (2) to identify the variables that play a crucial role in brain health.Data for this cross-sectional analysis came from the Positive Brain Health Now (BHN) cohort, a Canadian cohort of people aging with HIV. NA was conducted with 30 items selected from the brain related domains of fatigue, cognition, depression, sleep, anxiety, and motivation.The small-world properties of the network structure indicate that brain health variables are interconnected and may be influenced by shared underlying factors. The centrality indices suggest that items related to enjoyment of life and negative feelings may be particularly important for understanding brain health in this population.This study contributes to the growing body of knowledge on network approaches in diverse domains, demonstrating the utility of NA in conceptualizing complex constructs such as brain health
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".