A Hierarchical Cluster Analysis of Childhood Interpersonal Trauma and Dispositional Mindfulness: Heterogeneity of Sexual and Relational Outcomes in Adulthood
Bibliographic record
Abstract
The current mixed-method study aimed to 1) identify different childhood interpersonal trauma (CIT) and dispositional mindfulness (DM) profiles in an adult sample; 2) illustrate these profiles with qualitative data documenting childhood sexual abuse (CSA) and CIT survivors’ perceptions of their own DM; and 3) examine profile differences on sexual and relational outcomes. Participants were 292 adults who completed an online questionnaire. A subsample of participants having reported a history of CSA (n = 51) also completed semi-structured interviews. Hierarchical cluster, comparison, and content analyses were performed. Analyses yielded three profiles: 1) Lower victimization, high mindfulness; 2) Psychological victimization, low mindfulness; and 3) Multi-victimization, low mindfulness. Participants in profile 1 presented the lowest frequency of CIT experiences and the highest levels of DM and sexual and relational well-being. Profile 2 participants presented higher sexual and relational well-being (i.e., higher sexual satisfaction, lower sexual depression, and fewer interpersonal conflicts) than those in profile 3. By documenting distinct CIT and DM profiles and tying them to different levels of relational and sexual well-being, this study could guide practitioners in designing tailored interventions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".