Rebuilding what has been ruined: Navigating the impacts of child maltreatment for romantic relationships and friendships in emerging adulthood
Bibliographic record
Abstract
BACKGROUND: Child maltreatment (CM) can contribute to attachment insecurity, creating challenges to building supportive relationships that support growth and resilience in adulthood. Studies have focused on understanding how CM survivors adapt to the impacts of CM for their family-of-origin relationships or parenting, but fewer have explored chosen relationships. OBJECTIVE: This study seeks to fill this gap by exploring how emerging adults with CM histories subjectively experience, understand, and cope with the impacts of CM for their friendships and romantic relationships. PARTICIPANTS AND SETTING: The sample consisted of 23 emerging adults (18 to 25 years old) with a CM history, living in Canada. METHODS: Participants were invited for semi-structured interviews documenting CM and interpersonal experiences. A hybrid thematic analysis was conducted to identify themes and subthemes. RESULTS: Three overarching themes emerged, each dealing with broken trust: 1) Internal experiences stemming from CM preventing trust, 2) Coping with broken trust by protecting oneself, and 3) Learning how to build trust in others and themselves. CONCLUSIONS: Emerging adults described interpersonal challenges stemming from broken trust caused by CM and maintained by internal experiences of fear, longing, guilt, and shame. Coping through avoidance, isolation, and dominance over others further maintained distrust. However, emerging adults undertook a learning process, involving attempts to engage with others, recognize patterns of abuse, and practice behavioural changes. Future research should explore the role of chosen relationships, especially friends, in mental health interventions and efforts to foster greater social support and stronger community among emerging adults with CM.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".