The Assessment of Children with Attachment Disorder: The Randolph Attachment Disorder Questionnaire, the Behavioral and Emotional Rating Scale, and the Biopsychosocial Attachment Types Framework
Bibliographic record
Abstract
Children with attachment disorder (AD) have an ongoing risk of mental health challenges and an exacerbated resistance to traditional treatments. The inability to trust and inadequate relationship skills present a substantial challenge for supervising adults in families, child welfare, juvenile justice, public schools, and other community settings. This study examined the assessment of AD in children between ages 6 and 18 utilizing two standardized instruments, the Randolph Attachment Disorder Questionnaire and the Behavioral and Emotional Rating Scale. A new framework developed by the author, Biopsychosocial Attachment Types (BAT), for conceptualizing childhood attachment concerns, was explored as a foundation for assessment and as a guide for an incremental corrective experiential approach for altering the child’s internal working model of attachment. Biophilia and Attachment theories were explanatory for the BAT. This dissertation explores three research questions. First, can scores on the BERS be used to predict attachment disorder as measured by the RADQ? Second, can the three categories or six subcategories of the BAT be measured using selected BERS items plus additional author-developed items? Finally, if selected BERS items plus additional author-developed items are found to measure the BAT categories, are the resulting measures reliable and valid? The Foster Family Survey questionnaire completed by 285 foster parents of children 6 to 18 years in foster care for over three months in British Columbia, Canada, provided the data. Reported results of these analyses included an 18-item BAT measure and a 7-item subscale which predicted RADQ scores using selected items from the BERS with an additional pool of author-developed questions. The regression equation for the RADQ score predicted from the BERS Strength Quotient yielded an adjusted r2 of .268 while the best-fit model predicted from the BAT yielded a cumulative adjusted r2 of .515. The resulting BAT measure achieved an alpha score of .91 and factor analysis distinguished the subcategories. All of these results supported the value of continued research in this urgently needed area of investigation.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".