Alone with my phone? Examining beliefs about solitude and technology use in adolescence
Bibliographic record
Abstract
In this study, we examined how technology impacts adolescents’ perceptions of, and affective responses to solitude, as well as how adolescents’ own motivations for solitude (shyness, affinity for aloneness) were related to these reactions. Participants were N = 437 adolescents (297 girls; M age = 16.15 years, standard deviation ( SD) = .50) who were presented with a series of hypothetical vignettes asking them to imagine themselves in the context of pure solitude (alone in their room with the door closed), as well as being physically alone but engaged in increasing levels of virtual social engagement, including passive (e.g., watching videos, scrolling, but no direct social engagement), active (e.g., texting), and audio-visual (e.g., Facetime) technology use. Following each vignette, participants reported their perceptions of being alone and positive/negative affective responses. We also measured general motivations for solitude (shyness, affinity for aloneness). Among the results, adolescents perceived themselves as less alone in vignettes depicting increasing virtual social engagement. Affective benefits of increased virtual engagement were also found (e.g., less loneliness/boredom/sadness, greater social connection/contentment). However, these effects were moderated by solitude motivations, with different patterns evident as a function of participant shyness and affinity for aloneness. Findings highlight the importance of considering the nature of adolescents’ technology use when alone, as well as motivations for solitude, when considering links between solitude and well-being.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".