Use of oncology electronic learning resources to learn about geriatric oncology.
Bibliographic record
Abstract
11033 Background: Despite the aging population driving cancer growth, oncology trainees receive little training in geriatrics. While electronic resources, such as ASCO University, may help meet this gap, use of available geriatric oncology (GO) modules is low. We sought to understand why by exploring how oncology trainees currently learn about GO, their preferred methods for learning about GO, and their attitudes towards e-learning and geriatrics. Methods: Canadian medical oncology residents and recent graduates were electronically surveyed about the following domains: demographics, self-directed learning practices, use of electronic resources, perceived facilitators and barriers to e-learning, and geriatric oncology teaching. Descriptive statistics were used to analyze the data. Results: Respondents (n = 47) were mostly aged < 35 (66%). Respondents felt that learning about older adults was important (mean 4.3±1.0 out of 5) and generally felt comfortable caring for them (mean 3.9±0.9 out of 5) despite minimal training in geriatrics.Almost half (48.9%) received 0-2 hours of teaching in GO during residency, with the majority (59.6%) receiving teaching in clinic, 36.2% through lectures and 21.3% via seminars. Respondents also learned about GO through reading journal articles (42.6%), modelling in clinic (36.2%), reading a textbook (19.2%) or attending a conference (19.2%). Respondents preferred to learn about GO through on-site lectures (46.8%), dinner meetings (42.6%), case discussion (42.6%) and attending conferences (38.3%). Although overall respondents highly valued electronic learning (mean 4.3±0.75 out of 5), only a minority (8.5%) had received GO teaching electronically using e-modules and only 23.4% respondents were aware of e-learning resources in GO. In contrast most respondents (83%) had used an e-learning resource to learn about oncology. The most common oncology e-resources used were ASCO University (61.7%), Oncology Education (61.7%), and ASCO meeting videos (44.7%). Conclusions: Although oncology trainees value and commonly use e-learning resources, e-learning is not a common or preferred way to learn about GO, potentially due to lack of awareness about these resources. Future research will explore whether the current methods of educating oncology learners about older adults are appropriate and sufficient, as well as how trainees value and prioritize learning about topics that are not included in the formal curricula.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.036 | 0.005 |
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".