Effectiveness of Combined Copying Skills Training and Pharmacological Therapy for Patients with Migraine
Bibliographic record
Abstract
Headache is one of the most common complaints in neurological clinics. The current study carried out to determine the benefits of combined Coping Skills Training (CST) and Pharmacotherapy (Ph) for patients with migraine. Forty patients with migraine recruited from the outpatient clinics of Zahedan University of Medical Sciences( Iran) and randomly assigned to one of two treatment groups: the first group received combined coping skills training (CST) and pharmacotherapy(Ph); and the second group received the pharmacotherapy alone(Ph). Five patients due to lack of regular presence or filling out the questionnaires excluded from the study. Finally, the results of 35 subjects were analyzed. Data collection was done using the World Health Organization Quality of Life Questionnaire, General Self-Efficacy Scale-Sherer, Ways of Coping Questionnaire and Migraine Headache Index. The results of ANCOVA on post-test, after controlling the pre-test scores, suggested a significant difference in self-efficacy scores between CST+Ph and Ph groups. Moreover, results of ANCOVA did not show significant differences between the two groups in the scores of pain severity, quality of life, and the use of coping strategies. Findings of the present study indicated that coping-skills training, as a psychological intervention, improved self-efficacy. Further longitudinal studies are needed to confirm this conclusion.
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) | 0.001 | 0.001 |
| 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) | 0.003 | 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".