Working status and seasonal meteorological conditions predict physical activity levels in people living with HIV
Bibliographic record
Abstract
= 447) from six cities in the United States and matched their activity data with abstracted local meteorological data from National Oceanic and Atmospheric Administration (NOAA) weather reports. Participants were purposively recruited in 3-month blocks, from December 2015 to October 2017, to reflect physical activity engagement across the seasons. We calculated total physical activity (minutes/week) based on 7-day physical activity recall. Mild correlations were observed between meteorological factors and correlated with lower physical activity. Participants were least active in autumn (Median = 220 min/week) and most active in spring (Median = 375 min/week). In addition to level of education and total hours of work, maximum temperature, relative humidity, heating degree day, precipitation and sunset time together explained 17.6% of variance in total physical activity. Programs assisting in employment for PLHIV and those that promote indoor physical activity during more strenuous seasons are needed. Additional research to better understand the selection, preferences, and impact of indoor environments on physical activity is warranted.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".