An Expert Evaluation of Oncology Website Resources for Use in Pediatric Oncology Clinical Nursing Education in Low-Resource Settings
Bibliographic record
Abstract
Background: Online healthcare information is often used by pediatric oncology nurse educators in low- and middle-income countries (LMICs) for teaching clinical nurses as part of their initial orientation or continuing education. Access to peer-reviewed nursing journals via paid subscriptions or sub-specialty nursing textbooks in these settings is rare. This project identified and evaluated websites appropriate for pediatric oncology nurse educators in LMICs for teaching staff nurses, and for clinical staff nurses engaging in self-directed learning. Method: A strategic Google search for childhood cancer websites and an appropriate scoring tool was conducted. The Currency, Relevance, Authority, Accuracy, and Purpose Test, along with a previously published scoring rubric that was further adapted by the authors for pediatric oncology were used. Pediatric content, language options, and reading levels were appraised. Results: Of 86 identified websites, 51 met the inclusion criteria for evaluation. Websites were classified as highly recommended ( n = 36), recommended ( n = 12), or not recommended ( n = 3) based on scores (range 14–30; maximum possible score = 30). Half offered content in multiple languages. Most websites were 9–10th-grade reading level. Discussion: Childhood cancer information appropriate for clinical nurse orientation and self-directed learning by LMIC nurses is available on free websites. Some information (diagnosis, chemotherapy, psychosocial support) is repeated across websites, while some is lacking (pediatric cancer genetics and health equity disparities). Reading levels are higher than recommended for health literacy. The reviewed websites were rarely peer-reviewed, inconsistently updated, and generally self-regulated. However, 48 websites on childhood cancer were deemed appropriate pediatric oncology clinical nursing education resources.
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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 | 0.039 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".