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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".