Virtual Teaching and Training Models in Pediatric Oncology: A Retrospective Study from an LMIC
Bibliographic record
Abstract
Abstract Introduction A multidisciplinary approach is essential for success in pediatric oncology treatment. Updated protocols, quality nursing care, psychosocial support, safe and standardized preparation of chemotherapy, infection control, and effective data management are key shareholders for the effective management of childhood cancer. The Department of Pediatric Oncology at Indus Hospital and Health Network (IHHN) initiated consistent teaching and trainings with the help of the My Child Matters Grant from Sanofi Espoir Foundation. These courses were conducted in person starting in 2019 and had to be postponed and restructured due to coronavirus (COVID-19) pandemic in early 2020. Objectives The aim of this study was to determine the impact of virtual teaching models for healthcare workers employed in pediatric hematology/oncology departments in low-resource settings. Materials and Methods After in-person courses in 2019, courses for all six disciplines (physicians, nursing, infection control, pharmacy, psychosocial care, and cancer registry) were conducted virtually starting December 2020, open to all and free of cost. A total of 878 registrations were obtained and 267 certifications given. Lectures with Q&A sessions were conducted via zoom and recordings shared through email. Each course was conducted by the relevant department at IHHN with pre- and postassessment conducted through Google Forms. Session feedback was taken through zoom polls and a comprehensive course feedback conducted after completion; e-certificates were awarded to successful participants according to a predetermined criterion. Results A total of 434 physicians' registrations were done from around Pakistan and countries like Saudi Arabia, Malaysia, Jordan, and Canada for the online physicians' course, of which 110 received certifications after completing post-test and attendance criteria of 55%. Pharmacy, infection control, psychosocial care, and cancer registry courses saw participation and certification of 51, 41, 24, and 14 participants, respectively. Online sessions received positive feedback in terms of instructors, course content, convenience, and access from over 90% participants. Conclusion Due to the ease in coordinating hectic schedules and cost-effectiveness of online lectures, this virtual teaching model will persist despite the trajectory of the COVID-19 pandemic. Similar ventures aimed at pediatric oncology teaching and training are needed in a widespread manner to improve outcomes of childhood cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".