Predicting Patterns of Problematic Smartphone Use among University Students: A Latent Class Analysis
Bibliographic record
Abstract
University students are consistently ranked among the highest users of smartphones. As such, recent research has focused on examining the antecedents and consequences of problematic smartphone use among university students. While this work has been instrumental to our understanding of the risk and protective factors of developing problematic smartphone use, it has been largely variable-centered and thus fails to recognize the diversity with which problematic smartphone use is experienced among university students. As such, this study employed a person-centered approach (i.e., latent class analysis) to classify individuals based on patterns of problematic smartphone use feature/symptom cooccurrence among a sample of 403 Canadian university students. The relationships between these subgroups (or classes) and potential covariates (i.e., self-regulation, attachment anxiety, and attachment avoidance) were then examined to gain a more complete understanding of university students’ experiences of problematic smartphone use. Three classes of problematic smartphone use were identified: (1) “connected” displaying the features/symptoms of problematic smartphone use associated with being constantly connected to smartphones; (2) “problematic” displaying all of the features/symptoms of problematic smartphone use; (3) “distracted” displaying the features/symptoms associated with being distracted by smartphones. Findings indicate that attachment anxiety and avoidance were significantly associated with membership in the most pathological (i.e., “problematic”) class, suggesting that this may be an especially important risk factor for developing problematic smartphone use among university students. Moreover, self-regulation was significantly related to membership in the least pathological class (i.e., “connected”) suggesting that this may function as an important protective factor in developing more concerning patterns of problematic smartphone use. Findings from this work provide empirical evidence of a heterogeneity in patterns of problematic smartphone use associated with distinct individual-level risk factors. This has important implications for conceptualizations of problematic smartphone use and the development of intervention and prevention efforts.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".