Is the cluster risk model of parental adversities better than the cumulative risk model as an indicator of childhood physical abuse?: findings from two representative community surveys
Bibliographic record
Abstract
BACKGROUND: Screening strategies for childhood physical abuse (CPA) need to be improved in order to identify those most at risk. This study uses two regionally representative community samples to examine whether a cluster or cumulative model of risk indicators (i.e. parental divorce, parental unemployment, and parental addictions) explains a larger proportion of the variation in CPA. METHODS: Data were drawn from Statistics Canada's National Population Health Survey (1994-1995) and Canadian Community Health Survey 3.1 (2005). Response rates were greater than 80% in both samples. Each survey had approximately 13,000 respondents aged 18 and over who answered questions about the above adverse childhood experiences. RESULTS: A gradient was shown with similar outcomes in each data set. Only 3.4% of adults who experienced none of the three risk indicators reported they had been physically abused during childhood or adolescence. The prevalence of CPA was greater among those who experienced parental divorce alone (8.3%-10.7%), parental unemployment alone (8.9%-9.7%) or parental addictions alone (18.0%-19.5%). When all three risk indicators were present, the prevalence of CPA ranged from 36.0%-41.0% and the age-sex-race adjusted odds were greater than 15 times that of individuals with none of the three risk indicators. The cluster model explained a statistically significantly larger proportion of the variation than the cumulative model although the difference between the two models was modest. For the purposes of parsimony, the cumulative model may be the better alternative. CONCLUSIONS: Adults who were exposed to two or more childhood risk indicators were much more likely to report that they were physically abused during their childhood than those with only one or no risk factors. Medical professionals may use this information on cumulative risk factors to more effectively target screening for potential CPA. Future research should include prospective studies.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".