Education Research: Targeting Self-Described Knowledge Gaps to Improve Functional Neurologic Disorder Education Among Clinicians
Bibliographic record
Abstract
Background and Objectives: The objective of this study was to improve functional neurologic disorder (FND) education by identifying knowledge gaps among providers who registered for an online course on FND. The field of FND is rapidly evolving with new frameworks for understanding the diagnosis, pathophysiology, and treatment. This leads to the potential for knowledge gaps among clinicians who care for patients with FND. The shift away from terminologies such as "psychogenic" or "conversion" disorders underscores advances in how FND is conceptualized. Yet, gaps in the assimilation of this new knowledge among medical providers have been consistently found in surveys. This study is a qualitative analysis, allowing participants to state their specific knowledge gaps and identify content areas most in need of additional education. Methods: Providers from various disciplines including neurologists and other physicians, psychologists, and physical therapists enrolled in a virtual course containing 9 asynchronous lectures on various FND topics followed by 2 live webinars (fndsociety.org/fnd-education/virtual-education-course). Participants were invited to optionally submit questions for the live webinars to the expert panel about the care of FND in various treatment settings. A qualitative descriptive research design was used, with conventional content analysis applied to identify themes from participant questions. Results: One hundred ninety-one responses were collected from 268 participants over 2 months for a 71% response rate. Participant responses clustered on specific clinical presentations (e.g., functional seizures [FSs]), communication challenges with patients and other providers, inpatient challenges (e.g., when admission might be warranted), and outpatient challenges, such as limited access to multidisciplinary teams. Some participants explicitly stated outdated attitudes about FND. Discussion: Qualitative analysis of the participant responses revealed priority areas of knowledge gaps, indicating potential underexplored avenues for high-impact education on FND. These areas include diagnostic uncertainty, such as the presence of comorbid medical illness, FSs, and tools to help the patient when best practice models are not available. Developing case-based learning to better foster illness scripts and modules on psychoeducation and psychological treatments for the nontherapist FND provider would enhance existing educational tools to allow providers in every setting to better care for patients with FND.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".