Longitudinal Associations Between Scripted Attachment Representations in Late Adolescence and Depression in Adulthood in a Normative and a Higher-Risk Cohort
Bibliographic record
Abstract
Attachment representations are increasingly operationalized as secure base script knowledge —one’s level of awareness of the temporal-causal schema that summarizes basic features of seeking and receiving effective support from caregivers when in distress. A method was recently developed to assess secure base script knowledge during the Adult Attachment Interview (AAI sbs ), though its predictive validity for mental health outcomes is largely unknown. A series of pre-registered analyses, leveraging two large, longitudinal cohorts to assess the associations between AAI sbs and depression in emerging adults revealed that higher AAI sbs at age 18 years in the normative-risk sample, but not in a higher-risk cohort, predicted fewer depressive symptoms at age 30 years. In general, these associations were robust to concurrent depressive symptoms, sociodemographic and cognitive functioning covariates, and other traditional attachment representation measures. Findings support the importance of considering schematic attachment representations in developing interventions to improve young adults’ well-being.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".