The Longitudinal Association Between Habitual Smartphone Use and Peer Attachment: A Random Intercept Latent Transition Analysis
Bibliographic record
Abstract
Although many peers socialize online, there is evidence that adolescents who spend too much time online are lonely, depressed, and anxious. This study incorporates habitual smartphone use as a new way of measuring smartphone engagement, based on the shortcomings of simply measuring 'hours spent online'. Drawing on a large 2-year longitudinal study, the current research aims to investigate the association between habitual smartphone use and peer attachment among Canadian adolescents. A whole-school approach combined with a convenience sampling method was used to select our sample. A total of 1303 Canadian high school students (Grades 8-12; mage = 14.51 years, SD = 1.17 years; 50.3% females) who completed both waves of data collection were included in this study. A random intercept latent transition analysis (RI-LTA) was utilized to assess the association between habitual smartphone use (absent-minded subscale of the Smartphone Usage Questionnaire) and transition probabilities among profiles of peer attachment (Inventory of Parent and Peer Attachment), after adjusting for age, gender, ethnicity, stress, family attachment, school connectedness, and social goals. Three profiles of peer attachment were identified: (Profile 1: weak communication and some alienation; Profile 2: strong communication, strong trust, and weak alienation; Profile 3: okay communication and high alienation). Results of multivariable RI-LTA indicated that increased habitual smartphone use was significantly associated with a heightened probability of transitioning from Profile 2 at Wave 1 to Profile 1 at Wave 2 (odds ratio (OR) = 1.21, 95% confidence interval (CI) 1.003-1.46). Findings indicate that adolescents who are more habituated to their phones may become less attached to their peers over time. This offers insights for caregivers to focus on management and discussing smartphone engagement with adolescents to strengthen their attachment with peers.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".