A Nursing Intervention Increases Quality of Life and Self‐Efficacy in Migraine: A 1‐Year Prospective Controlled Trial
Bibliographic record
Abstract
OBJECTIVES: To compare the impact of a combined nursing and medical approach to a medical follow-up only on headache outcomes, quality of life, and self-efficacy in a cohort of migraineurs. BACKGROUND: Interdisciplinary approaches have been proposed for migraine management. A nursing intervention could improve patient outcomes. METHODS: We prospectively studied new patients referred to our tertiary headache center for migraine. The control group was followed by a physician; the active group was also followed by a nurse with a personalized intervention including adaptation of the lifestyle. RESULTS: Two hundred patients (176 women and 24 men, mean age 40 years old) were included and classified according to headache frequency. Each group was followed for 12 months with daily headache diaries. One hundred and sixty-two completed the study. There were no significant differences between groups for the decrease in headache days, the percent of chronic patients reverting to episodic status or the cessation of medication overuse. Patients in the control group were more likely to find a successful prophylaxis (55.6 vs 27.7%, P = .002). Despite this, the mean decrease in HIT-6 scores at month 8 was 5.23 ± 9.18 for the active group compared with a decrease of 2.10 ± 9.27 for the control group (P = .030, clinically significant difference of 3.13). Headache Management Self-Efficacy Scale (HMSE) scores, representing the feeling of self-efficacy, increased by 14.35 ± 18.41 for the active group vs 4.69 ± 21.22 in the control group (P = .002). CONCLUSION: A nursing intervention can lower the impact of migraines on the patient's life. The improvement in the HIT-6 score in this study was correlated with improvements in self-efficacy.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".