P032: Identifying the bleeding and thrombosis learning needs of the Free Open Access Medical education (FOAM) community
Bibliographic record
Abstract
Introduction: Developing structured online educational curricula that meet learner needs is challenging. Thrombosis and bleeding are areas of innovation and change in emergency medicine. We aimed to determine the learning needs of the Free Open Access Medical education (FOAM) community with the subsequent goal of developing structured curricula to meet them. Methods: A Massive Online Needs Assessment (MONA) was conducted to determine the perceived and unperceived educational needs in thrombosis and bleeding. The survey was designed by a multidisciplinary team of experts and was open from September 20 to December 10, 2016. The survey requested limited demographic information and contained questions to identify topics of interest. Respondents’ baseline knowledge and unperceived needs were assessed using 5 case scenarios containing 3 questions each. Knowledge gaps were defined a priori as topics where <50% of participants answered correctly. Results: We received 198 complete responses by staff physicians (n=109), residents (n=46), medical students (n=29) and allied health professionals (n=14) from 20 countries. 116/198 responses were from people working in emergency medicine. Topics of interest to participants included choice of anticoagulants, interruption of anticoagulation, management of bleeding and monitoring anticoagulation. Knowledge gaps were identified in 4 main areas including interruption of anticoagulation, management of bleeding (including reversal of anticoagulation and massive transfusion), inherited thrombophilia, and screening for malignancy in acute thrombosis. Conclusion: We have identified six priority topics to cover in our future online Thrombosis and Bleeding curriculum by surveying the online medical community. Although perceived and unperceived needs showed high congruence, two priority topics were only identified by assessing unperceived needs.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Scholarly communication Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | medium |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".