51 PedsCases Quality Improvement User Survey
Bibliographic record
Abstract
PedsCases (www.pedscases.com), founded by medical students in 2008, is a free open-access medical education resource for pediatric learners. Currently, the website has 140 podcasts, 70 interactive cases, 11 clinical videos and a number of additional learning tools. PedsCases has experienced considerable growth over the past five years with an increase in content production, exposure and collaboration with the Canadian Paediatric Society. Our goal is to continue to develop innovative learning tools and resources to help learners meet their pediatric learning objectives. To explore the accessibility and utility of PedsCases learning tools and resources; To assess the alignment between PedsCases learning resources and curriculum objectives of paediatric learners at various stages in training, and; To identify and explore avenues for innovation based on user feedback. We designed a 20 question online survey for PedsCases users to inform the above objectives. Non-identifiable demographic information including country of residence, level of training, and career goals were collected. Through both open and closed ended questions, participants were asked to share information about the accessibility of PedsCases resources as well as content appraisal. The form was pilot tested before implementation. An online survey platform, Google Forms, was used to collect and store data. A total of 57 responses were collected over a 6-week period (September 1, 2018 to October 17, 2018). The majority of respondents were Canadian (84.2%). Majority of respondents were either postgraduate residents (35.1%, n=57), or senior medical students (38.6%, n=57). Amongst all participants 77.2% indicated that PedsCases content consistently met their learning objectives. Within the subset of postgraduate residents, this number increased to 85% (n=20). Forty-three percent indicated that PedsCases is a suggested learning resource by their program. Quick summary sheets, guideline summaries and podcasts were identified as preferred resources. Feedback from users focused on improving website functionality including updating the web interface and reorganizing content. Content on PedsCases is meeting the learning objectives of senior medical students and postgraduate trainees. Learners consider podcasts, guideline summaries, and quick summary sheets to be the most useful resources. Based on the feedback received, we plan to continue to develop podcasts, but to invest greater resources into developing quick reference material to meet learner demands. We will also use feedback from the user survey to help design a new and updated PedsCases website.
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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 | 0.012 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".