Factors associated with cyber-victimization among immigrants and non-immigrants in Canada: a cross-sectional nationally-representative study
Bibliographic record
Abstract
OBJECTIVES: There is a paucity of research on patterns of cyber-victimization in minority groups, including immigrants. This study aimed to identify individual, interpersonal and contextual characteristics associated with cyber-victimization among immigrants and non-immigrants. METHODS: We drew on nationally representative data from adolescents and adults in the Canadian General Social Survey on victimization (2014). We used multivariable logistic regression to identify potential factors associated with cyber-victimization in the last 12 months, stratified by immigrant status and sex. RESULTS: Among 27,425 survey respondents, the weighted prevalence of cyber-victimization in the last 12 months was 2.1% among immigrants and 2.3% among non-immigrants. Cyber-victimization rates differed significantly by sex among immigrants (2.8% for males vs. 1.4% for females), but not among non-immigrants (2.1% for males vs. 2.4% for females). While most other factors associated with cyber-victimization were similar for immigrants and non-immigrants, there were pronounced associations of past child maltreatment (adjusted prevalence odds ratio [aPOR] 4.85, 95% confidence interval [CI] 2.76, 8.52) and residence in an unwelcoming neighbourhood (aPOR 5.08, 95% CI 2.44, 10.55) with cyber-victimization among immigrants that were diminished or absent among non-immigrants. Additionally, sex-stratified analyses among immigrants showed cyber-victimization to be strongly associated with having a mental health condition (aPOR 3.50, 95% CI 1.36, 8.97) among immigrant males only, and with perceived discrimination (aPOR 4.08, 95% CI 1.65, 10.08), as well as being under 24 years old (aPOR 3.24, 95% CI 1.09, 9.60) among immigrant females. CONCLUSIONS: Immigration status and sex were differentially associated with cyber-victimization. Findings support the salience of a social-ecological perspective and gender-stratified analyses to better elucidate complex pathways linking cyber-victimization to potential gender-based health inequities among immigrants.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".