Age, Pain Intensity, Values-Discrepancy, and Mindfulness as Predictors for Mental Health and Cognitive Fusion: Hierarchical Regressions With Mediation Analysis
Bibliographic record
Abstract
Background: Several studies have confirmed that higher levels of psychological flexibility predict better functioning for those suffering with chronic pain. However, few studies have investigated the role of the individual components of psychological flexibility within a chronic pain population in relation to aging specifically and the related indirect mediational processes. Aims: The present study aimed to compare how age, pain intensity, mindfulness, and values-discrepancy predicted mental health and cognitive fusion separately. It also explored the indirect process relations through the use of a mediated analysis. Method: 233 participants completed an online survey which included demographical questions as well as the following questionnaires; Short Form McGill Pain Questionnaire (SF-MPQ); General Health Questionnaire 12; Cognitive Fusion 7-Item Questionnaire (CFQ-7); Mindfulness Attention Awareness Scale (MAAS); and the Chronic Pain Values Inventory (CPVI). The relationships from the responses of the questionnaires and demographics were then analysed through two hierarchical regression models followed by further mediation analysis. Results: In the first model, values-discrepancy, pain intensity, and mindfulness all predicted mental health, but age did not. However, age did account for a significant portion of the variance in the second model when cognitive fusion was used as the dependent measure (DV). It was also found that cognitive fusion mediated the relationship between age and mental health. Conclusions: These results are discussed within the context of using indirect process relations of psychological flexibility and third wave therapies such as Acceptance and Commitment Therapy (ACT) for a chronic pain population.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".