Exploring the prevalence and correlates of identity theft-related preventive measures among U.S. adolescents
Bibliographic record
Abstract
Adolescents represent an emerging population at risk of identity theft, yet little is known about their engagement in preventive behaviours. Using data from a nationally representative sample of U.S. adolescents who participated in the National Crime Victimization Survey – Identity Theft Supplement (NCVS-ITS), this study examined the prevalence of six identity theft-related preventive behaviours and assessed the influence of demographic and contextual factors on the likelihood of engaging in these practices. Descriptive findings revealed low overall adoption of preventive behaviours, even among adolescents with financial accounts. Logistic regression analyses indicated that having a checking/savings account and, to a lesser extent, owning a credit card were the most consistent and significant predictors of engagement in preventive behaviours. Age and household income also emerged as significant predictors in several models, while race, gender, and ethnicity were largely nonsignificant. The implications of the findings are discussed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".