Making a new-patient headache education session more patient-centered: what participants want to know
Bibliographic record
Abstract
Purpose: To describe the new-patient Education Session provided by the Calgary Headache Assessment and Management Program, analyze patient evaluations, and generate potential patient-centered improvements based on themes in patient feedback.Materials and Methods: Between 2008 and 2012, 1873 new patients attended the Education Session, and 913 evaluations were completed. Session objectives ratings were analyzed. Open-ended questions regarding most- and least-helpful components and suggestions for improvement were examined using thematic analysis.Results: Eighty-seven percent of respondents indicated they would recommend the session to others with headache. Median objectives ratings ranged from 9.0–10.0 out of 10 and were stable over time. Most-helpful themes included medication, types of headache, our program’s multi-faceted management approach, medication overuse, triggers, and not feeling alone. Most respondents left the least-useful and suggestions sections blank or commented “nothing” or “not applicable”. Least-useful themes included migraine overemphasis, insufficient or excessive medication content, participant over-disclosure, and lack of practical trigger management strategies.Conclusion: Most attendees found the Education Session useful. Those who did not provided valuable input that will allow us to modify the content. Our findings may benefit other headache programs seeking to implement or improve patient education programing. Implications for RehabilitationHeadache is a common and debilitating condition.Education is an important part of headache treatment, and has been associated with decreases in headache frequency, intensity, and disability, as well as increases in self-efficacy.A new-patient Education Session is a practical and inexpensive way to provide evidence-based medical and behavioral headache information.Quantitative and qualitative analysis of patient evaluations can help gauge relevance and direct content changes.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".