The relation between smartphone use and everyday inattention.
Bibliographic record
Abstract
In two studies, we explored the relation between subjective reports of smartphone use and eve-ryday inattention. We created two questionnaires that measured general smartphone use (i.e. how frequently people send and receive texts, use social media, etc), and absent-minded smartphone use (i.e. how frequently people use their phone without a purpose in mind). In addition, partici-pants completed four scales assessing everyday attention lapses, attention-related errors, sponta-neous mind wandering and deliberate mind wandering, which were included in order to measure everyday inattention. The results of both studies revealed a strong positive relation between gen-eral and absent-minded smartphone use. Furthermore, we observed significant positive relations between each of the smartphone use questionnaires and each of the four measures of inattention. However, a series of regression analyses demonstrated that when both types of smartphone use were used as simultaneous predictors of inattention, the relation between inattention and smartphone use was driven entirely by absent-minded use. Specifically, absent-minded smartphone use consistently had a unique positive relation with the inattention measures, while general smartphone use either had no relation (Study 1) or a unique negative relation (Study 2) with inattention.
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 | 0.002 | 0.016 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".