A needs assessment survey to inform a general practitioner in oncology (GPO) training program in Nepal.
Bibliographic record
Abstract
11024 Background: Nepal is a low-income country with a population of over 30 million that faces a significant burden of cancer, with approximately 60,000 patients receiving a cancer diagnosis per year. However, a lack of cancer care providers is recognized as a major barrier to cancer care in Nepal. To address this gap, a national GPO training program may be an effective task-shifting strategy to increase the oncology workforce. A needs assessment survey was conducted to determine baseline oncology training for Nepalese General Practitioners (GPs) and the feasibility and desirability of establishing a GPO training program in Nepal. Methods: A survey was distributed to 171 GPs in Nepal. The survey was distributed via the REDCap electronic database, a secure-web-based software platform based at Queen’s University in Kingston, Ontario, Canada. Reminders were sent out via social media accounts. The survey results were recorded anonymously from February to July 2021. Results: Data was collected from 71 GPs in Nepal. Data revealed significant gaps in oncology training, with only 6% of respondents reporting undergoing a mandatory oncology rotation during their training, and 15% indicating their training adequately prepared them to care for patients with cancer. Although respondents reported seeing an average of 1-5 cancer patients per day, only 9% reported providing chemotherapy treatment, and 36% indicated regular provision of follow-up cancer care. Only 1 in 5 general practitioners reported having radiation oncology services available at their institution or an institution within the same zone. 96% of GPs surveyed agreed that there is a need for a GPO program, and 71% reported they would be very interested in participating in this type of training. A majority of respondents indicated a preference for self-directed and small-group learning as the preferred modes of content delivery in a GPO training program. Conclusions: Our findings indicate a need and high level of desirability for a national GPO training program in Nepal. Further, the needs assessment provides insight into practitioner perspectives on information needs and content delivery. This data will be used to guide future curriculum development for the Nepal GPO program. [Table: see text]
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".