Accreditation of oncology education in low-and- middle income countries: Perspectives of African oncologists.
Bibliographic record
Abstract
e18213 Background: There is an expansion of postgraduate oncology training programs in LMIC . Locally trained oncologists are expected to deliver high quality care. Accreditation is one of the essential regulatory mechanisms to ensure high-quality education. Accreditation systems are rarely standardized or applied in the majority of LMIC. The purpose of this study is to understand the perspectives of African Oncologists on the role of accreditation and adoption of global standards into oncology training programs Methods: We developed a survey that addressed African Oncologists’ perspectives of the role of accreditation. It included 187 standards from the WFME PGME standards, ACGME-I standards for hematology/oncology, and the Royal College of Physician and Surgeons of Canada medical oncology standards. A 3-point scale was employed for each standard: 1 = not important, 2 = important but not essential, 3 = essential Results: The survey was sent to 79 physicians, 38 responded. 87% agreed that accreditation ensures quality of education. 100% agreed that it should involve an external review. 74% believe that accreditation is feasible in resource-constrained settings. 45% agreed it will not increase emigration of qualified doctors. Data of 22 individuals who completed the survey in its entirety were analyzed for standards. 5 standards received the highest ratings of 3 from all respondents: life-long learning, professionalism and ethical principles, competence in chemotherapy delivery and management of toxicities. One standard (prior internal medicine training) received a low rating of < 2.0. The majority of standards had ratings between 2.6 and 2.94, indicating that African Oncologists believe most standards to be useful. Ratings < 2.6 were related to resource constraints such as having PET scans or exposure to clinical trial patients. Conclusions: Most African oncologists believe that accreditation ensures quality of education. Most of the standards were considered important. This data will be useful for developing and adapting oncology education accreditation standards in resource-constrained settings. Abbreviations: ACGME-I: American Council of Graduate Medical Education-International
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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 | 0.014 | 0.023 |
| 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 | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".