Effectiveness of Mindfulness Training on Pain Perception, Cognitive Function, and Mental Well-Being in Migraine Patients
Bibliographic record
Abstract
The present study aimed to assess the effectiveness of mindfulness training on pain perception, cognitive function, and mental well-being in migraine patients in Tabriz. The research method was quasi-experimental with a pre-test–post-test design and separate groups. The statistical population included all migraine patients in Tabriz during the first four months of 2024. To determine the sample, 30 participants were non-randomly selected through purposive sampling and assigned to experimental and control groups. Pain perception was measured using the Revised Short-Form McGill Pain Questionnaire (SF-MPQ-2), cognitive function was assessed using the Cognitive Ability (Performance) Questionnaire by Najati (2013), and mental well-being was evaluated using the Cantril Ladder (1965). Prior to the intervention, both groups completed pre-tests on pain perception, cognitive function, and mental well-being. The experimental group received 8 sessions of Williams' (2002) mindfulness training through an educational protocol, while the control group received no educational intervention. After the intervention, both groups completed the same post-test measures. Data were analyzed using multivariate analysis of covariance (MANCOVA). The results showed that mindfulness training was effective in the subscales of pain perception, cognitive function, and mental well-being in migraine patients (p < 0.05). Based on the effectiveness of mindfulness training on pain perception, cognitive function, and mental well-being in migraine patients, it can be concluded that mindfulness training, alongside treatment of physiological components, can reduce pain perception and, through this, enhance cognitive function and mental well-being.
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 | 0.000 | 0.001 |
| 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".