Development and Validation of a Measure of Attachment Disorders Based on <i>DSM-5</i> Criteria: The Early TRAuma-Related Disorders Questionnaire (ETRADQ)
Bibliographic record
Abstract
Background: A review of the scientific literature showed few valid tools for assessing reactive attachment disorder (RAD) and disinhibited social engagement disorder (DSED), two diagnostic entities traditionally grouped under “attachment disorders.” The Early TRAuma-related Disorders Questionnaire (ETRADQ), a caregiver report, was developed to assess attachment disorders in school-age children based on the Diagnostic and Statistical Manual of Mental Disorders–Fifth edition criteria. This study sought to validate this instrument. Method: Caregivers of school-age children from the community ( n = 578) and caregivers of at-risk children adopted or in out-of-home care ( n = 245) completed a sociodemographic questionnaire, the ETRADQ, the Relationship Problem Questionnaire, the RADA ( RAD and DSED Assessment) interview, and the Barkley Functional Impairment Scale for Children and Adolescents. Results: Confirmatory factor analysis of the ETRADQ items supported the expected organization of the measure, that is, two second-order factors and five subfactors: (1) RAD scale (three subscales: Low selective attachment, Low social and emotional responsiveness, Emotional unpredictability) and (2) DSED scale (two subscales: Interactions with unfamiliar adults, Social disinhibition). All scales showed excellent internal consistency, test–retest reliability, convergent validity, and known-group validity. Conclusions: Results support the reliability and validity of the ETRADQ.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".