A Comparison of Two Methods to Assess the Usage of Mobile Hand-Held Communication Devices
Bibliographic record
Abstract
The purposes of this study were to: 1) examine agreement between self-reported measures of mobile device use and direct measures of use, and 2) understand how respondents thought about their device use when they provided self-reports. Self-reports of six categories of device use were obtained using a previously developed questionnaire, and direct measures of use were collected using a custom logging application (n = 47). Bland-Altman analyses were used to examine agreement between the two measurement approaches. Interviews targeted participants' experiences completing the device use section of the questionnaire. Self-reports of use on a typical day last week overestimated logged use. Overestimates tended to be low at low average usage times, and became more variable as usage time increased. Self-reports of use yesterday also exceeded logged use, however the degree of overestimation was less than for a typical day last week. Six themes were identified from interviews, including the thought process used by participants to arrive at usage and the ease of reporting usage. It is challenging for respondents of this questionnaire to provide accurate self-reports of use. The source of this challenge may be attributed to the intrinsic difficulty of estimating use, partly due to the multiple functions of the devices as well as the variability of use both within a day and a week. Research investigating the relationship between device use and health outcomes should include a logging application to examine exposure simultaneously with self-reports to better understand the sources of hazardous exposures.
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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 | 0.034 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".