Adolescents’ Digital Technology Use, Emotional Dysregulation, and Self-Esteem: No Evidence of Same-Day Linkages
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
Abstract Concerns regarding the potential negative impacts of digital technology use on youth mental health and well-being are high. However, most studies have several methodological limitations: relying on cross-sectional designs and retrospective reports, assessing technology use as an omnibus construct, and focusing on between- instead of within-person comparisons. This study addresses these limitations by prospectively following young adolescents ( n = 388) over a 14-day ecological momentary assessment study to test whether adolescents’ digital technology use is linked with self-reported emotional dysregulation and self-esteem and whether these relationships are stronger for adolescent girls than boys. We found no evidence that adolescents experienced higher emotional dysregulation ( b = − .02; p = .07) and lower self-esteem ( b = .004; p = .32) than they normally do on days where they use more technology than they normally do (within-person). Adolescents with higher average daily technology use over the study period did not experience lower levels of self-esteem (between-person, b = − .02; p = .13). Adolescents with higher average daily technology use across the two-week period did report higher levels of emotional dysregulation ( p = .01), albeit the between-person relation was small ( b = .08). There was no evidence that gender moderated the associations, both between and within adolescents ( b s = − .02–.13, p = .06 − .55). Our findings contribute to the growing counter-narrative that technology use does not have as large of an impact on adolescents’ mental health and well-being as the public is concerned about.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 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