P.024 Educational needs in migraine care: results from a mixed-methods study among Canadian primary care providers and specialists
Bibliographic record
Abstract
Background: Migraines are sub-optimally treated, affect millions of Canadians, and are underrepresented in medical training. A study was conducted to identify the needs of Canadian Healthcare Providers (HCPs) for migraine education, with the aim to inform the development of learning activities. Methods: This ethics-approved study was deployed in two consecutive phases using a mixed-methods approach. Phase 1 (qualitative) explored the causes of challenges to migraine care via a literature review, input from an expert working group, and semi-structured interviews with multiple stakeholders. Phase 2 (quantitative) validated these causes using an online survey. Results: The study included 103 participants (28 in phase 1; 75 in phase 2): general practitioners=37; neurologists=24; nurses=14; pharmacists=20; administrators, policy influencers and payers=8. Four areas of sub-optimal knowledge were identified: (1) Canadian guidelines, (2) diagnostic criteria, (3) preventive treatment, and (4) non-pharmacological therapies. Attitudinal issues related to the management of migraine patients were also identified. Detailed data including the frequencies of knowledge gaps among general practitioners and general neurologists will be presented along with qualitative findings. Conclusions: Educational activities for general practitioners and general neurologists who treat patients with migraines should be designed to address the four educational needs described in this study.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".