An Integrative Examination of Childhood Multiple Victimization through Ecological Lenses
Bibliographic record
Abstract
The landscape of the childhood victimization literature is shifting, with a growing number of researchers emphasizing the importance of designing studies that account for and aim to disentangle the interconnections among victimization experiences. This is a notable contrast to the bulk of the scientific inquiry to date, which has tended to examine victimization experiences in isolation from one another and has created victimization-specific models of risk. On the other hand, the multiple victimization field aims to better understand the overlap among risk factors and the co-occurrence across victimization experiences to create general or non-specific risk models for childhood victimization. From this field emerged the concept of multiple victimization (defined as exposure to more than one type of victimization within a specified time period), that has been established as the unfortunate norm among victimized children. The current dissertation was designed not only to help attain a better understanding of the phenomenon of childhood multiple victimization but also to contribute to our understanding of the frequency, co-occurrence, and risk (grounded in the ecological framework) of childhood multiple victimization. This dissertation addresses important shortcomings of the published literature, such as the scarcity of studies that account for the co-occurrence among victimization experiences, the limited victimization data on school-aged children and clinical samples, and the dearth of studies that test comprehensive risk models of multiple victimization. Caregivers of school-aged children (N = 213) in the Ottawa/Gatineau area participated in the online study, which involved the completion of a 30-minute questionnaire package that assessed their child’s victimization experiences as well as child (e.g., sex, age), family (e.g., caregiver psychosocial functioning, family functioning), and neighbourhood (e.g., safety) factors. Results provided support for the ubiquitous nature of childhood multiple victimization (in the past year and lifetime) as well as for the common co-occurrence of various victimization experiences. Specifically, while a certain overlap was found across all victimization forms, conventional crimes and peer and/or sibling victimization co-occurred most often in this school-aged sample. In addition, victimization forms that may be qualified as “severe” (sexual victimization, Internet victimization, maltreatment) tended to co-occur with many additional forms and were rarely reported on their own. Findings highlighted the important associations between victimization exposure and psychosocial difficulties (anxiety, depression, aggression, and posttraumatic stress), and weighting techniques (i.e., weighting severe victimization forms more heavily) were not found to significantly contribute to better predictability of psychosocial difficulties. Turning to the risk models, a number of correlates of childhood multiple victimization were identified, most notably family variables including family dysfunction, caregiver psychosocial functioning, and substance use problems. However, a number of correlates (particularly socio-demographic factors) were also found to vary according to the victimization experiences assessed, providing partial support for the specificity assumption whereby victimization risk models vary according to the victimization form assessed. The theoretical and applied implications of research findings for efforts aimed at addressing childhood multiple victimization were also 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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".