Applying an Ecological Framework to Factors Associated With Non-Spousal Sexual Assault Among Women in Canada
Bibliographic record
Abstract
An ecological framework is useful for understanding how individual, relationship, community, and societal level factors can affect women’s vulnerability to sexual assault. However, most studies have focused on examining individual and relationship factors only, due to measurement challenges and a lack of data at the societal level. The purpose of this study was to use data from a nationally representative victimization survey to identify salient predictors of sexual assaults not committed by common-law or marital partners among women across all levels of the ecological framework. Out of 16,738 female respondents, 1.9% ( n = 319) experienced one or more incidents of non-spousal sexual assault in the 12 months preceding the survey. Logistic regression modeling indicated that at the individual level, statistically significant factors associated with non-spousal sexual assault included age, Indigenous status, marital status, mental disability, education, main activity, and homelessness or precarious housing. Relationship level predictors of non-spousal sexual assault were a history of child sexual abuse and witnessing violence between parents. Community level predictors were a weak sense of belonging in the local community, low likelihood that neighbors would contact police if they witnessed family violence in the respondents’ home, and high likelihood that neighbors would contact police if they witnessed other criminal behavior. Societal level factors were perceptions that local police do a poor job promptly responding to calls, perceptions that local police do a poor job treating people fairly, and having experienced discrimination in the previous 5 years. The results demonstrated that community and societal level factors are critical components of an ecological framework and are important to understanding and addressing the many factors which are independently associated with vulnerability to sexual assault.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".