Development of a Childhood Attachment and Relational Trauma Screen (CARTS): a relational-socioecological framework for surveying attachment security and childhood trauma history
Bibliographic record
Abstract
BACKGROUND: Current psychometric measures of childhood trauma history generally fail to assess the relational-socioecological context within which childhood maltreatment occurs, including the relationship of abusers to abused persons, the emotional availability of caregivers, and the respondent's own thoughts, feelings, and actions in response to maltreatment. OBJECTIVE: To evaluate a computerized approach to measuring the relational-socioecological context within which childhood maltreatment occurs. METHOD: The psychometric properties of a Childhood Attachment and Relational Trauma Screen (CARTS) were evaluated as a retrospective survey of childhood maltreatment history designed to be appropriate for completion by adults. Participants were undergraduates (n=222), an internet sample (n=123), and psychiatric outpatients (n=30). RESULTS: The internal reliability, convergent, and concurrent validity of the CARTS were supported across samples. Paired differences in means and correlations between rated item-descriptiveness to self, mothers, and fathers also accorded with findings of prior attachment and maltreatment research, illustrating the utility of assessing the occurrence and effects of maltreatment within a relational-socioecological framework. CONCLUSIONS: Results preliminarily support a new survey methodology for assessing childhood maltreatment within a relational-socioecological framework. Further psychometric evaluation of the CARTS is warranted.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".