A unique, interactive and web-based pediatric rheumatology teaching module: residents’ perceptions
Bibliographic record
Abstract
BACKGROUND: The limited availability of pediatric rheumatologists for teaching in pediatric residency programs negatively impacts resident education about rheumatology. At present, there are no educational websites available for trainees to learn about pediatric rheumatology. We are planning to develop an interactive web-based teaching module to improve resident learning about rheumatology ("POINTER": Pediatric Online INteractive TEaching in Rheumatology). The aim of this study was to perform a needs assessment of pediatric residents who will be using POINTER. METHODS: Pediatric residents (n = 60) at The Hospital for Sick Children were emailed an online survey. This was designed to assess prior use of online teaching modules, the utility of an online teaching module for rheumatology and which technologies should be included on such a site. RESULTS: Forty-seven residents participated in the survey (78.3% response rate). Ninety-one percent of the respondents thought that an interactive teaching website would enhance their learning and should include case-based teaching modules. Several web-based technologies were felt to be important for inclusion on the teaching modules. These included graphics and animation (86.4%), interactivity (93.2%), pictures (100%), live digital videos (88.9%) and links to articles and research (88.6%). CONCLUSIONS: An interactive web-based rheumatology teaching module would be well utilized by pediatric residents. Residents showed preference for case-based teaching modules as well as multimedia modalities for learning a detailed musculoskeletal examination.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".