A Lifespan Development Theory of Insecure Attachment and Internalizing Symptoms: Integrating Meta-Analytic Evidence via a Testable Evolutionary Mis/Match Hypothesis
Bibliographic record
Abstract
Attachment scholars have long argued that insecure attachment patterns are associated with vulnerability to internalizing symptoms, such as depression and anxiety symptoms. However, accumulating evidence from the past four decades, summarized in four large meta-analyses evaluating the link between insecure attachment subtypes and internalizing symptoms, provide divergent evidence for this claim. This divergent evidence may be accounted for, at least in part, by the developmental period under examination. Specifically, children with histories of deactivating (i.e., insecure/avoidant) but not hyperactivating (i.e., insecure/resistant) attachment patterns in infancy and early childhood showed elevated internalizing symptoms. In contrast, adolescents and adults with hyperactivating (i.e., insecure/preoccupied) but not deactivating (i.e., insecure/dismissing) attachment classifications showed elevated internalizing symptoms. In this paper, we summarize findings from four large meta-analyses and highlight the divergent meta-analytic findings that emerge across different developmental periods. We first present several potential methodological issues that may have contributed to these divergent findings. Then, we leverage clinical, developmental, and evolutionary perspectives to propose a testable lifespan development theory of attachment and internalizing symptoms that integrates findings across meta-analyses. According to this theory, subtypes of insecure attachment patterns may be differentially linked to internalizing symptoms depending on their mis/match with the developmentally appropriate orientation tendency toward caregivers (in childhood) or away from them (i.e., toward greater independence in post-childhood). Lastly, we offer future research directions to test this theory.
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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 | 0.080 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.023 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".