The Perspective of the Oncology Health Care Professionals on Educational Conferences
Bibliographic record
Abstract
BACKGROUND: Continuous education is vital to update the knowledge of the health care professionals (HCPs) engaged in cancer management. We aim to explore the perspective of the oncology HCPs on oncology conferences. METHODS: A questionnaire was handed to the delegates and faculty of an oncology educational scientific conference in the Kingdom of Saudi Arabia (KSA). The questionnaire explored the perspective on the ideal number, duration and the educational value of such conferences. RESULTS: Medical oncologists represented 70.5% of the 78 responders. All responders attended oncology conferences in the preceding year with 24.4% have attended >9 conferences. The educational value of these conferences was rated 7.98/10. Two thirds are satisfied with the frequency of conducted event or desire more. Short (half to one day) meetings are preferred by 66.67% and the mean number of optimum educational events is 5.7 per year. Educational events were highly valued (score ≥ 8/10) as venues to meet colleagues. CONCLUSION: The findings suggest that the current frequency and scientific value of oncology conferences and educational events are satisfactory to the majority of the relevant HCPs in the KSA. More CME events of up to one day in duration seem to be desired.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".