The use of the internet for medical information and a needs assessment for a web based educational resource: A survey of Canadian medical oncology trainees and training program directors
Bibliographic record
Abstract
17022 Background: Medical Oncology trainees develop their skills and knowledge through formal educational sessions, independent learning and clinical rotations. The internet serves as a source of up to date information and a potential educatinal resource. Despite the existence of many websites with oncology related information, there has not been a comprehensive assessment of how medical oncology trainees and program directors use the internet to meet educational objectivs. Method: In the first phase of developing a new educational website (OncologyEducation.com), we surveyed medical oncology trainees and program directors from training programs across Canada to assess how they accessed the internet to determine the elements they considered essential for a trainee-oriented site. Results: 12 out of 13 Canadian medical oncology training programs participated in our survey. A total of 12 program directors and 23 trainees responsed to our survey for a 74.5% response rate. 71.4% of respondents spend up to 10 hours per week on the internet for work related reasons. Pubmed and UptoDate were the most frequently visited sites. Respondents reported using the internet for email (97.1%), answering clinical questions (88.6%), accessing practice guidelines (80%), and literature updates (71.4%). Respondents expressed a need for an educational website stressing the following content: (1) Key updates by disease sites (2) Access to pivotal journal articles (3) Access to upcoming conferences/information (4) Links to other medical sites/medical oncology sites (5) Fellowship Opportunities. Conclusion: The internet is an important resource for supplementing the training of medical oncology trainees. The development of an educational website based on the needs assessed in this survey is warranted. Upon development of the website it will be evaluated for effectiveness and impact on oncology training and clinical practice. No significant financial relationships to disclose.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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, 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".