Nonpharmacological Self-Management of Migraine Across Social Locations: An Equity-Oriented, Qualitative Analysis
Bibliographic record
Abstract
BACKGROUND: Migraine is a disabling neurological disorder and the sixth biggest cause of disability worldwide. The World Health Organization has declared migraine a major public health problem due to a paucity of knowledge about cause and effective treatment options. Both in incidence and severity, migraine disproportionately affects people occupying marginalized social locations (SL). Managed pharmacologically, migraine is treated with daily preventive and as-needed abortive medications. Both come with high literal and figurative costs: intolerable side effects, medication interactions, and prohibitive prices. Cost prohibitive, ineffective, and unsustainable pharmacological treatment options have contributed to high levels of interest in complementary approaches by people with migraine, but little is known about their motivations, patterns of use or access, or how these may vary by SL. METHOD: We conducted focus groups with 30 people with migraine to explore their desires and recommendations for migraine clinicians and researchers. We used qualitative content analysis to identify themes.Outcomes: We identified 4 themes: a more holistic, collaborative, long-term treatment approach; medication as a short-term solution; high personal and economic costs of medication; and desire for more information and access to natural approaches. Across SL, participants expressed keen interest in integrative approaches and wanted better access to complementary modalities. Participants in marginalized SL described reliance on traditional/folk remedies, including engagement with family and community healers, who they described as more affordable and culturally accessible. CONCLUSIONS: Holistic and integrative approaches were preferred over medication as long-term migraine management strategies. However, people in marginalized SL, while disproportionately disabled by migraine, did not feel as comfortable accessing integrative approaches through currently available channels. Engaging with these communities and using a critical lens to explore barriers to access can develop options to make complementary modalities more approachable, while also attending to systemic blind spots that may unintentionally alienate socially marginalized groups.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".