A survey of Canadian medical oncologists on internet use for medical information and a needs assessment for an oncology education website
Bibliographic record
Abstract
17072 Background: The internet offers an innovative resource for medical oncologists to share and discuss important medical information and advances. Despite the existence of many websites with oncology related information, there has not been a comprehensive assessment of how Canadian medical oncologists use the internet to access this information. Purpose: In the first phase of developing a new educational website (OncologyEducation.com), we surveyed Canadian medical oncologists to determine how they used the internet to access information and to assess their needs for a web based educational resource. Method: A structured survey was developed and assessed for face and content validity by medical oncologists from our local institution. The survey had several domains including: comfort level with computers, description of internet use for work, and key features they would want in an educational website. The survey was sent to all medical oncologists in Canada via regular mail and e-mail. Results: 58 % (144/247) of medical oncologists responded to our survey. The number of years in practice varied from <5 yrs (31%) to 5–10 yrs (30%), and 11–20 years (15%) with 85% having an academic appointment. 90% of respondents were comfortable with using a computer. The most common reasons for internet use were email (91%), literature updates (78%), and answering clinical questions (72%). 27% of respondents used the internet for clinical questions daily, and 29% on a weekly basis. 49% accessed the internet for work-related information for 1–5 hours weekly, 26% for 6–10 hours weekly, and 20% for more than 11hrs weekly. Respondents expressed a need for an educational website stressing the following content: (1) Canadian Oncologists Database to improve opportunities for collaboration (2) Key updates by disease sites (3) Access to information about upcoming conferences. Conclusion: The internet plays a major role in the daily clinical activities of Canadian medical oncologists. The development of an education website based on the needs assessed in this survey is warranted. We are currently developing this site (OncologyEducation.com) and plan to evaluate on an ongoing basis. 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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 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) | 0.003 | 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".