The Role of Optimism and Pessimism in Chronic Pain Patients Adjustment
Bibliographic record
Abstract
This study analyses the relationships between patients' dispositional optimism and pessimism and the coping strategies they use. In addition, the coping strategies repercussions on adjustment to chronic pain were studied. Ninety-eight patients with heterogeneous chronic pain participated. The assessment tools were as follows: Life Orientation Test (LOT), the Vanderbilt Pain Management Inventory (VPMI), the McGill Pain Questionnaire (MPQ), Hospital Anxiety and Depression Scale (HADS), and the Impairment and Functioning Inventory for Chronic Pain Patients (IFI). The hypothetical model establishes positive relationships between optimism and the use of active coping strategies, whereas pessimism is related to the use of passive coping. Active coping is associated with low levels of pain, anxiety, depression and impairment and high levels of functioning. However, passive coping is related to high levels of pain, anxiety, depression and impairment and low levels of functioning. The hypothetical model was empirically tested using the LISREL 8.20 software package and the unweighted least squares method. The results support the hypotheses formulated regarding the relations among optimism, pessimism, coping and adjust of chronic pain patients. By analysing optimism among chronic pain patients, clinicians could make better predictions regarding coping and adjustment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".