Avoidance and endurance coping after mild traumatic brain injury: patterns of coping and associated psychological functioning
Bibliographic record
Abstract
PURPOSE: According to the avoidance-endurance model of chronic pain, avoidance and endurance behaviors are distinct pathways to disability following injury. Both coping styles are associated with poor outcomes post-mild traumatic brain injury (mTBI), but whether they represent distinct pathways to disability is unclear. We assessed whether distinct patterns of avoidance and endurance coping exist post-mTBI (study 1) and examined their convergent/divergent validity in an independent sample (study 2). METHODS: = 41.6) with persisting mTBI symptoms and high avoidance/endurance behavior completed measures of avoidance, endurance, and psychological functioning. Individuals were clustered using cluster centers from study 1. Cluster differences in psychological functioning were assessed with ANOVAs. RESULTS: Four clusters emerged in study 1: high-avoidance/high-endurance (group 1), low-avoidance/low-endurance (group 2), mid-avoidance/low-endurance (group 3), and mid-avoidance/high-endurance (group 4). In study 2, after controlling for symptom burden, group 1 had higher catastrophizing and thought suppression compared to groups 3 and 4. CONCLUSIONS: Four poorly defined clusters were identified. The single cluster-defining factor associated with poorer psychological functioning was avoidance behavior, suggesting it may be an important treatment target.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".