PAIN NARRATIVE AS AN EDUCATIONAL TOOL: USE OF THE MCGILL PAIN QUESTIONNAIRE IN PAIN NEUROSCIENCE EDUCATION
Bibliographic record
Abstract
INTRODUCTION Pain is a complex and multifaceted experience, traditionally defined by the International Association for the Study of Pain (IASP) as “an unpleasant sensory and emotional experience associated with actual or potential tissue damage, or described in terms of such damage”1. This definition underscores the dual nature of pain as encompassing both physical-biological and mental-cognitive dimensions. Pain is commonly assessed using one-dimensional scales (e.g., NRS, VAS), which, while practical, are limited in capturing the full complexity of the pain experience. The Italian version of the McGill Pain Questionnaire (MPQ), comprising 78 descriptors divided into sensory, affective, and evaluative categories2, enables a richer characterisation of pain. Moreover, it provides patients with an opportunity for reflection and greater awareness of their pain. This context aligns with Pain Neuroscience Education (PNE), an approach that aims to educate patients by explaining the neurobiological and psychosocial underpinnings of pain. The objective of this study is to explore the use of the MPQ not only as an assessment tool but, more importantly, as an educational instrument within a PNE framework, as part of an integrated therapeutic approach that includes pharmacological and rehabilitative interventions. METHODS The MPQ was administered to 32 patients attending the Pain Therapy Unit at Taormina Hospital (ASP Messina). Participants included individuals suffering from musculoskeletal, oncological, and neuropathic pain. The MPQ was administered orally by a clinician who read the descriptors aloud, asking patients to select those most representative of their pain experience. This interactive process facilitated a semi-structured interview that accompanied the completion of the questionnaire. RESULTS During the administration of the questionnaire, many patients spontaneously expressed reflections on triggering, evocative and contextual factors of their pain. The words chosen often triggered associations with emotional events, specific memories or environmental situations. In this way, the MPQ served not only as an evaluative tool but also as an educational opportunity. These unprompted reflections represent a potential clinical benefit within the PNE framework: by contextualising pain, patients begin to reinterpret their experience not only in sensory terms but also cognitively and emotionally. The identification of triggers is not limited to pathophysiological mechanisms but frequently involves personal meanings, thereby promoting a deeper awareness of pain’s multidimensional nature. CONCLUSIONS The integration of the MPQ within a relational and guided context proved to be useful not only for qualitative pain assessment but also for stimulating patient awareness and reflective processing. This approach aligns well with the principles of PNE, facilitating a broader understanding of pain from a biopsychosocial perspective. The findings suggest that the dialogical use of the MPQ may serve as a valuable educational tool within a comprehensive therapeutic pathway that includes pharmacological and rehabilitative interventions. Further research is warranted to evaluate its impact on clinical pain management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".