Emotional affection on a sustained attention task: The importance the aging process and depression
Bibliographic record
Abstract
Chronic pain is a complex experience that has now become a major public health issue. This has prompted many researchers to study attention, understanding it to be a crucial factor that allows altering the experience of pain, while attributing considerable importance to sustained attention. Accordingly, the main studies in this field stress the importance of emotion regulation processes and emotions on the perception of painful stimuli and attentional processes themselves. Nevertheless, only a handful of studies have been found that directly study the relationship between these variables. Within this context, this article sets out to analyse emotional regulation processes, emotional variables (depression and anxiety), the experience of pain, and age on the ability to maintain the vigilance response in a sample of patients with chronic pain. This involved selecting a sample of 49 patients with rheumatoid arthritis and examining their performance in an ad-hoc sustained attention test. With a view to complying with the study's main purpose, the participants were also assessed through the use of the following self-report measures: the Beck Depression Inventory (BDI-I); the Hospital Anxiety and Depression Scale (HADS); the McGill Pain Questionnaire, and the Difficulties in Emotion Regulation Scale (DERS). Linear regression analyses revealed a significant impact of the aging process on the performance times in the attention task. Likewise, age and depression recorded a significant correlation with the mistakes made during the task. These results suggest that higher depression levels and an older age might be related to a worse adaptation to pain management techniques based on attention processes, such as mindfulness.
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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 | 0.003 |
| 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 | 0.000 | 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".