Does effort-cost decision-making relate to real-world motivation in people living with HIV?
Bibliographic record
Abstract
INTRODUCTION: Low motivation is frequent in older people with HIV, yet poorly understood. Effort-cost decision-making (ECDM) tasks inspired by behavioral economics have shown promise as indicators of motivation or apathy. These tasks assess the willingness to exert effort to earn a monetary reward, providing an estimate of the subjective "cost" of effort for each participant. Here we sought evidence for a relationship between ECDM task performance and self-reported motivation in a cross-sectional study involving 80 middle-aged and older people with well-controlled HIV infection, a chronic health condition with a high burden of mental and cognitive health challenges. METHODS: Participants attending a regular follow-up visit for a Canadian longitudinal study of brain health in HIV completed a computerized ECDM task and a self-report measure of motivation. Other brain health measures were available, collected for the parent study (cognition, depression, anxiety, and vitality, as well as self-reported time spent on real-world leisure activities). RESULTS: Contrary to our hypothesis, we found no relationship between ECDM performance and self-reported motivation. However, those willing to accept higher effort in the ECDM task also reported more time engaged in real-world activities. This association had a small-to-moderate effect size. CONCLUSIONS: The behavioral economics construct of subjective cost of effort, measured with a laboratory ECDM task, does not relate to motivation in people living with chronic HIV. However, the task shows some relationship with real-world goal-directed behavior, suggesting this construct has potential clinical relevance. More work is needed to understand how the subjective cost of effort plays out in clinical symptoms and everyday activities.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".