The Impact of Cognitive Anxiety and the Rating of Pain on Care Processes in a Vigilance Task: The Important Part Played by Age
Bibliographic record
Abstract
Chronic pain is a serious public health problem that has grown exponentially in recent years, which is why it has received the attention of numerous researchers. Most of the studies in the field of chronic pain have focused on care as a mediating variable on the perception of painful stimuli and emotions. Nevertheless, there are very few studies that have gone in the opposite direction. This study's aim is therefore to analyse the impact of emotional variables (anxiety and depression), the rating of pain, and age on vigilance processes in a sample of patients with chronic pain. To do so, the attentional performance of a cohort of 52 patients with chronic pain was measured through the use of a modified dot-probe task. Furthermore, all the participants were evaluated using the following self-report measures: Beck's Depression Inventory-II (BDI-II), the McGill Pain Questionnaire, and the Pain Anxiety Symptoms Scale-20 (PASS-20). Stepwise multiple linear regression analysis revealed a significant negative correlation between the pain rating index and the number of mistakes the participants made during the attention test. There was also a positive and significant correlation with age and another negative and significant correlation with cognitive anxiety regarding the overall performance times during the undertaking of the experimental task. These results point to the importance of a more in-depth understanding of the impact that the emotional variables and other variables such as age have on attentional processes and the rating of pain. Finally, the discussion focuses on the implications these results could have for clinical practice or for future research studies in this field.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".