The association between childhood adversity and resilience among cadets: Evidence from The Royal Canadian Mounted Police Study
Bibliographic record
Abstract
In this study, our aims were to (a) examine the associations between adverse childhood experiences (ACEs; i.e., childhood maltreatment, household challenges, and peer victimization) and resilience, adjusting for sociodemographic variables, and (b) test the moderating effect of emotional regulation on the association between childhood maltreatment ACEs and resilience in Royal Canadian Mounted Police (RCMP) cadets. Study data were from the RCMP Study. Participants were Cadets (N = 597, 75.0% men) who underwent a full assessment before the Cadet Training Program (CTP). Logistic regression models were used to estimate the associations between ACEs and resilience while adjusting for sociodemographic variables and to test the moderating effect of emotional regulation; adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated. Several childhood maltreatment ACEs were associated with decreased resilience among cadets after adjustment for sociodemographic covariates: physical abuse, aOR = 0.26, 95% CI [0.10, 0.68]; emotional abuse, aOR = 0.28, 95% CI [0.10, 0.79]; neglect, aOR = 0.22, 95% CI [0.09, 0.55]; exposure to intimate partner violence, aOR = 0.18, 95% CI [0.04, 0.73]; and peer victimization, aOR = 0.30, 95% CI [0.12, 0.76]. The interaction between exposure to any childhood maltreatment ACE and emotional suppression, as assessed using the Emotion Regulation Questionnaire, was significantly associated with low resilience scores, aOR = 0.94, 95% CI [0.89, 0.99]. The current results underscore the critical importance of mitigating the adverse impact of childhood maltreatment ACEs on resilience processes among cadets before the CTP.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".