Is Self‐Report of Attachment Patterns in Young People Stable From Three to Nine Months After a Concussion?
Bibliographic record
Abstract
Insecure adult attachment patterns have recently been associated with an increased risk of developing persistent post-concussion symptoms (PCS) and poorer treatment outcomes, highlighting the potential of attachment-informed treatment protocols for persistent PCS. A key requirement for such approaches is that attachment patterns remain relatively stable during the post-injury illness course, allowing them to serve as reliable indicators for guiding initial care and treatment planning. This study aimed to assess the stability of self-reported attachment anxiety and attachment avoidance in individuals aged 15-30 years, from three (T1) to nine (T2) months after a concussion, corresponding to the period when treatment for PCS typically becomes relevant. This study is a methodological follow-up of a larger clinical cohort study comprising 3080 individuals aged 15-30 years who were diagnosed with a concussion. Attachment dimensions were measured using the Experiences in Close Relationships-Relationship Structures Questionnaire at three (T1) and nine (T2) months post-injury. Both mean-level and individual changes were examined, taking relevant covariates such as sex, age, level of PCS, post-traumatic stress disorder symptoms, and social support into account. At T1, 958 respondents were included (39%), with 416 also responding at T2 (43%). Mean-level changes were minimal (attachment anxiety: β = -0.07 (95% confidence interval [CI]: -0.22; 0.08)), attachment avoidance: β = 0.00 (95% CI: -0.11; 0.11). Larger individual changes that could not be explained by measurement error were observed in 12% of participants. No association was found with any of the covariates. Self-reported attachment patterns appear to remain stable in most young people with a concussion from 3 to 9 months post-injury. The findings strengthen confidence in conducting research to investigate whether attachment-informed treatment approaches can improve healthcare for these patients.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".