Daily self‐reports resulted in information bias when assessing exposure duration to computer use
Bibliographic record
Abstract
BACKGROUND: Self-reported exposure duration to computer use is widely used in exposure assessment, and this study examined the associated information bias in a repeated measures setting. METHODS: For 3 weeks, 30 undergraduate students reported daily cumulative computer-use duration and musculoskeletal symptoms at four random times per day. Usage-monitor software installed onto participant's personal computers provided the reference measure. We compared daily self-reported and software-recorded duration, and modeled the effect of musculoskeletal symptoms on observed differences. RESULTS: The relationships between daily self-reported and software-recorded computer-use duration varied greatly across subject with Spearman's correlations ranging from -0.22 to 0.8. Self-reports generally overestimated computer use when software-recorded durations were less than 3.6 hr, and underestimated when above 3.6 hr. Experiencing symptoms was related to a 0.15-hr increase in self-reported duration after controlling for software-recorded duration. CONCLUSIONS: Daily self-reported computer-use duration had a weak-to-moderate correlation with software-recorded duration, and their relationship changed slightly with musculoskeletal symptoms. Self-reports resulted in both non-differential and differential information bias.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".