Validation of the Reactive Attachment Disorder and Disinhibited Social Engagement Disorder Assessment (RADA): A <i>DSM</i> -5 Semistructured Interview
Bibliographic record
Abstract
Very few empirically validated tools exist for assessing reactive attachment disorder (RAD) and disinhibited social engagement disorder (DSED). The RAD and DSED assessment interview (RADA), a semistructured diagnostic interview, was updated in 2018 from the CAPA-RAD interview to reflect the diagnostic criteria changes in the Diagnostic and statistical manual of mental disorders (5th ed.; DSM-5 ). The aim of this study was to validate the RADA on school-age children in Canada. Caregivers of 5 to 12-year-old children from the community ( n = 98), in foster care ( n = 147), and in residential care ( n = 123) completed the RADA interview and a series of questionnaires. Confirmatory factor analysis (CFA) of the RADA interview supported a four-factor structure similar to the DSM-5 symptom clusters. A short “strictly DSM-5 ” version of the RADA showed a two-factor structure—RAD and DSED—and an excellent fit to the data. Scales of both structures showed good-to-excellent internal consistency, interrater reliability, convergent validity, and known-group validity. Classifying the children yielded RAD and DSED rates of <1% and 18%, respectively, for children in foster care and 7% and 10%, respectively, for children in residential care. This study supports the validity of the RADA interview for school-age children and is the first to provide RAD and DSED rates for children in residential care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".