Neurology Health Advocacy Curriculum: Needs Assessment, Curricular Content and Underlying Components
Bibliographic record
Abstract
BACKGROUND: Lack of a health advocacy curriculum and clarity are obstacles for effectively teaching neurology health advocacy (NHA) to neurology residents. Our purpose is to assess the need and develop content for a NHA curriculum and to describe its underlying components. METHODS: This is a cross-sectional study with two steps. In step one, neurologists and neurology residents at University of Calgary were surveyed about their perception of teaching NHA and asked to rank 56 neurological diseases on a Likert scale based on how well they lend themselves to teaching health advocacy. In step two, curricular items were developed for the top five neurological diseases, using a modified Delphi procedure. The reliability of the survey instrument was determined by Cronbach's alpha. Exploratory factor analysis was used to identify the underlying components of NHA. RESULTS: Forty-six neurologists and 14 neurology residents were surveyed, with a response rate of 88.33%. Fifty-six percent of neurologists and 85% of residents believe that NHA curriculum is needed. The top five neurological presentations, that lend themselves easily to teaching NHA were: stroke/transient ischemic attacks, alcoholism, epilepsy, Alzheimer's disease, and multiple sclerosis. The survey instrument reliability was 0.97. Exploratory factor analysis revealed four factors that can explain the variability in the survey instrument: multidisciplinary approach to neurological disorders, prevention of recurrence of neurological disease, collaboration with other medical subspecialties, and communication with professions outside the medical field. CONCLUSION: Neurologists' and residents' responses support that NHA curriculum is needed. Four components of NHA were identified that can be used for teaching NHA as well as health advocacy in general practice.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".