Decision Making in Migraine Patients Taking Sumatriptan: An Exploratory Study
Bibliographic record
Abstract
Until recently, much of the medical and psychological literature has examined and conceptualized the taking of medication from the viewpoint of adherence to or compliance with recommendations from health professionals. However, some authors have argued that medication taking is mostly determined by patient decision making. In order to investigate the factors and processes influencing the patient's decision to take or not take abortive therapy for migraines, 20 migraineurs (according to International Headache Society criteria) were asked, using a semistandardized interview, what factors influenced their decision to take or not take sumatriptan when they had a migraine. Qualitative analysis revealed a 2-stage decision-making process. First, the patient collects information from interoceptive and environmental cues (symptom monitoring) to predict whether the headache that is beginning will become a migraine. Then, if the patient decides it is a migraine, he or she weighs various factors to decide whether to take sumatriptan. These results are consistent with the current cognitive psychology literature on decision-making processes and could lead to significant improvements in understanding the process by which patients make decisions about taking sumatriptan and, ultimately, could lead to better patient education and more effective headache control. They also open a whole new field in the empirical investigation of medication-taking behavior.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".