Exploring resilience in the affect regulation of family violence-exposed adolescents
Bibliographic record
Abstract
Objectives: The study explores the presence of the three components of Ungar’s (2019) biopsychosocial process definition of resilience in the context of family violence-exposed adolescents’ descriptions of affect regulation when experiencing high affect arousal. Methods: A convenience sample of 16 youth, age 15-25 with histories of family psychological, and/or physical violence exposure, completed semi-structured qualitative interviews describing affect regulation during arousal states in past stressful situations. Interviews were recorded and transcribed verbatim. Utilising deductive framework analysis, predefined thematic coding was conducted in NVivo. Results: Rich descriptions were generated of youth’s adaptive capacities to regulate affect while under stress. We explored the presence of the three components of Ungar’s (2019) resilience definition in the data: 1) Risk affect regulation during hyper-/hypo-arousal states, 2) Navigation of access to and negotiation for meaningful promotive and protective internal and external factors, and 3) Resilience outcomes of recovery, adaptation, and transformation. The framework analysis of Ungar’s (2019) resilience definition illuminated differential interactions between adolescents and access to resources in their environments. Despite some resource deficits, participants demonstrated adaptive resilience when regulating affect. Implications: Ungar’s (2019) process resilience definition highlights the interconnection between youth’s resource needs and the capacity of their environments to provide them to enhance resilience. Results suggest that interventions to increase resilience should incorporate the full biopsychosocial ecological process model with a focus on regulation capacity. The knowledge gained from youth perspectives of affect regulation processes is directly applicable to complex trauma-informed interventions to increase self-regulation and resilience while reducing behavioural reactivity for violence-exposed adolescents.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".