Assessing trainee’s need and readiness for e-cancer education and training in Africa.
Bibliographic record
Abstract
11012 Background: As a prerequisite to launching a virtual education and training platform, the African Organization for Research and Training in Cancer (AORTIC) assessed the educational needs and e-Learning readiness of Sub-Sahara African oncology staff before designing ICT support systems. Methods: Using existing assessment models, we developed a self-assessment tool for a cross-sectional survey. Competence areas assessed include basic knowledge, early detection and diagnosis, clinical skill, cancer registry, and cancer management respectively. Components of e-Learning readiness assessed include access to technology, technological skills/competence, capacity for self-directed learning, confidence prerequisite skills, and motivation. We calculated an average score for each component as (Sum of positive response/Sum of responses). Results: There were 128 respondents. 33%, 44%, 48%, 58% and 60% felt they needed further training in the areas of basic knowledge, early detection and diagnosis, clinical skill, cancer registry, and cancer management respectively. Reported internet access for e-Learning is 100%, significantly higher than the 31.2% internet penetration of Sub-Sahara Africa. However, only 15.7% have a bandwidth of quality to support real-time visual learning. Weighted averages for technological access, skill, competence, capacity for self-directed learning, motivation, and prerequisite skills was 69%, 86%, 51% 42% and 80% respectively. Conclusions: There are deficiencies in the training of oncology health professionals in all five competency areas, and e-Learning can supplement ongoing traditional training to meet the gap. The low access to the internet in Africa may not be disruptive to asynchronous oncology training but may limit real-time video-based synchronous learning which involves more intense interaction between trainees and trainers. However, African bandwidth growth trend indicates this limitation is fast diminishing. African oncology health professionals are technologically competent for e-Learning and score high in the prerequisite skills. There is, however, a low ability for self-directed learning, and many may need a support system for the advanced virtual education.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".