Psychological mediators of avoidance and endurance behavior after concussion.
Bibliographic record
Abstract
OBJECTIVE: The avoidance-endurance model (AEM) proposes multiple pathways from acute to chronic pain, with distinct cognitive and behavioral components in each pathway. The AEM may also be applicable to persistent symptoms after concussion. In this study, we tested the AEM as an explanatory framework for concussion outcomes, by using mediation analyses through the proposed psychological mechanisms. Based on the AEM, we hypothesized that postconcussion symptoms would significantly predict avoidance behavior through catastrophizing, and endurance behavior through thought suppression and self-distraction. PARTICIPANTS AND METHODS: = 41.8 years old, 63% female) who completed measures of postconcussion symptoms, catastrophizing, thought suppression, "self-distraction" (Five Factor Mindfulness Questionnaire "Act with Awareness" Scale reverse-scored), avoidance behavior, and endurance behavior at an average of 17.8 weeks postconcussion. We conducted 3 mediation analyses to assess each of the AEM pathways. RESULTS: We found a significant indirect effect of postconcussion symptoms on avoidance behavior through catastrophizing (ab = .113 (.036), 95% CI [.053, .195]). The indirect effects of postconcussion symptoms on endurance behavior through thought suppression (ab = .011 (.012), 90% CI [.002, .035]) and "self-distraction" (ab = .003 (.009), 90% CI [.008, .022]) were not statistically significant. CONCLUSIONS: Results supported the catastrophizing-avoidance pathway in concussion, but not the thought-suppression-endurance or self-distraction-endurance pathways. Therefore, catastrophic thinking about concussion symptoms may be an appropriate treatment target for individuals who exhibit fear-avoidance behavior. Further research is needed to establish whether thought suppression and self-distraction are relevant for interventions aimed at reducing excessive endurance behavior. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".