From Pain to Pleasure - Investigating the Role of Islam in Alleviating Pain and Enhancing Pleasure in Self-Identifying Muslim Undergraduate Students: A Research Protocol
Bibliographic record
Abstract
Introduction: The current research protocol investigates the role of Islam in the experience of pain and pleasure, exploring whether religious priming has any capacity to transform a painful experience to a pleasurable one in undergraduate students. The purpose is to assess if religious beliefs can facilitate cognitive reappraisal, making painful stimuli less distressing and potentially enjoyable. Methods: The study will consist of 100 self-reported Muslim undergraduate students from Canadian Universities. Participants’ religiosity levels will be assessed prior to the study. Participants will be exposed to electrical stimulation under two conditions: religious priming and control priming. Facial electromyography (fEMG) activity in the zygomaticus major (associated with pleasure) and corrugator supercilii (associated with pain) will be recorded. Subjective ratings of pain (via the Visual Analogue Scale) and pleasure (via the Snaith-Hamilton Pleasure Scale) will also be collected. A within-subjects counterbalanced design will be employed, and statistical analyses will compare the fEMG activity and subjective ratings between the two priming conditions. Results: Religious priming will be associated with increased pleasure on a physiological level (i.e., increased zygomaticus major activity) and self-report basis compared to control priming. Religious priming will be also associated with decreased pain on a physiological level (i.e., decreased corrugator supercilii activity) and self-report basis compared to control priming. These effects will be moderated by religiosity levels. Discussion: The observed effects suggest that religious priming may facilitate cognitive reappraisal, allowing for the reinterpretation of painful experiences as pleasurable. This finding advocates for the utilization of religion as a potential tool for hedonic regulation. Conclusion: The current study fills a critical gap by empirically demonstrating that religious priming can transform painful experiences into pleasurable ones, an area with limited prior research. These findings offer promising implications for pain management and emotional well-being. Future research should consider comparing diverse religious contexts, broaden measurements beyond fEMG, and explore underlying mechanisms further.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.031 | 0.009 |
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".