The Effect of Attachment on the Association Between Familial Dysfunction and Recidivism and Aggression in a Sample of Adolescent Offenders: Testing a Moderated-Mediation Effect
Bibliographic record
Abstract
The main purpose of this study was to determine if familial dysfunction exerts gendered and indirect effects on general recidivism or aggression (relationally-driven aggression and indirect aggression) through attachment style.This question was tested using a crosssectional and longitudinal design with a sample of justice-involved youth (211 males, 101 females).Overall, results indicated that attachment did not mediate the effect of familial dysfunction on general recidivism or relationally-driven aggression and that gender did not moderate this relationship.Interestingly, there was evidence to support the indirect effect of familial dysfunction on indirect aggression through preoccupied attachment but only for males.If future research replicates these results, preoccupied attachment should be implemented as a male-specific treatment target for practitioners and as a screening tool for risk assessment developers.Keywords: attachment theory, familial dysfunction, recidivism, gender, indirect aggression, relationally-driven aggression For me, the past two years of my education have been some of the most demanding, stressful, difficult, yet rewarding times in my academic career.Without the support of several individuals, it is doubtful that I would have had the motivation or the resources to complete this research.I would first and foremost like to thank my supervisor, Dr. Shelley Brown, for her constant support and guidance.Dr. Brown, I sincerely appreciate all the time and effort you have put into my academic success, it has not gone unnoticed.You are one of the most hard-working people I have ever met and I consider myself truly fortunate to have been one of your students.I would also like to thank Dr. Natalie Jones and Dr. Tina Daniels; the quality of this research is in part thanks to your thought-provoking questions and valuable insights.To my partner Connor, who is always pushing me to succeed and achieve my goals, I am so very grateful that your love and support was always there for me to fall back on in times of need.To my best friends, Emily and Katie, I am immensely thankful that despite time and distance, I can always count on you to inspire me and encourage me to
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".