<p>Are Cognitive Load and Focus of Attention Differentially Involved in Pain Management: An Experimental Study Using a Cold Pressor Test and Virtual Reality</p>
Bibliographic record
Abstract
OBJECTIVE: The aim of this study is to assess whether distraction (lack of attentional focus) and attention (cognitive load) are differentially involved in the analgesic effect of virtual reality (VR) immersions during a cold pressor test (CPT). METHODS: Thirty-one participants were randomly assigned to four experimental conditions (high and low cognitive load, attention with or without a reminder of the pain stimuli) and performed three CPTs. Pain was assessed based on the duration of the CPT (pain tolerance), a visual analog rating scale of perceived pain intensity during the CPT and the subjective pain scale of the Short form McGill Pain Questionnaire (SF-MPQ). RESULTS: The statistical analyses revealed that VR immersions were associated with less pain compared to the baseline (all p <0.001), but for the experimental manipulations, only the conditions where there was an increase in cognitive load (ie, from low cognitive load at Immersion 1 to high cognitive load at Immersion 2) were effective for increasing pain tolerance (significant Time X Conditions interaction). The interactions were not significant for pain intensity assessed with the VAS or the SF-MPQ. CONCLUSION: The results suggest that increases in cognitive load play an important role in the analgesic effect of VR immersion, although the combination of attentional focus and cognitive load may be important. Suggestions are given for designing a replication study.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".