The role of flow and media multitasking for problematic smartphone use and the different types of smartphone use
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study examines the relationship between flow and multitasking in the context of problematic smartphone use (PSU). PSU is the inability to regulate smartphone use, which has negative consequences. The study examines how flow, a state of intense concentration and immersion, contributes to PSU, mainly when facilitated by media multitasking - the simultaneous engagement in multiple media activities and how these factors are associated with the different types of uses. Data were collected from 374 participants, including 219 women, 154 men and one non binary person, with an average age of 31.72 years and mainly consisted of students. Standardized questionnaires were used to collect the data, including the Mobile Phone Problem Use Scale (MPPUS-10), screen time measurement, usage type scales, and the Media Multitasking-Revised Scale (MMT-R). The results of correlation analyses showed that procedural and habitual smartphone use significantly predict flow and media multitasking. Additionally, the results of mediation analyses revealed that media multitasking mediates the correlation between flow and PSU and between flow and screen time. These mediations emphasize the reinforcing effects of multitasking on flow and PSU. The study complements the current research with a meaningful new contribution on the role of media multitasking in problematic smartphone use behaviours and screen time, showing that its mediating role should be considered in interventions for healthier smartphone use. • Procedural and habitual smartphone use significantly predict flow and media multitasking. • Multitasking mediates the correlation between flow and PSU and between flow and screen time. • These mediations emphasize the reinforcing effects of multitasking on flow and PSU. • The findings highlight the need for targeted interventions that address media multitasking behaviors and the experience of flow and promote healthier smartphone use patterns.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it