P.103 Studying behaviors among neurosurgery residents using web 2.0 analytic tools
Bibliographic record
Abstract
Background: Web 2.0 technologies (e.g. blogs, social networks, and wikis) are increasingly being utilized by medical schools and postgraduate training programs as tools for information dissemination. These technologies offer the unique opportunity to track metrics of user engagement and interaction. Here, we employ Web 2.0 technologies to assess academic behaviors among neurosurgery residents. Methods: We performed a retrospective review of all educational lectures, part of the core Neurosurgery Residency curriculum at the University of Toronto, posted on our teaching blog ( www.TheBrainSchool.net ) from Sept 2013 - Nov 2016. We looked for associations with lecturer’s academic position, timing of examinations, and lecture/subspecialty topic. Results: The overall number of clicks on 123 lectures was 1079. Most of these clicks were occurring during the in-training exam month (43%). Click numbers were significantly higher on lectures presented by faculty (mean 18.6, SD ± 4.1) compared to residents-delivered lectures (mean 8.4, SD ± 2.1) (P= 0.031). Functional neurosurgery lectures were the most downloaded (47%), followed by pediatric neurosurgery (22%). Conclusions: The current study demonstrates the value of Web 2.0 analytic tools in examining residents study behavior. Residents tend to ‘cram’ downloading lectures in the same month of training exams and display a preference for faculty-delivered lectures.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".