Development of a Digital Patient Education Tool for Patients With Cancer During the COVID-19 Pandemic (Preprint)
Bibliographic record
Abstract
BACKGROUND Due to the COVID-19 pandemic, a large portion of oncology consultations have been conducted remotely. The maladaptation or compromise of care could negatively impact oncology patients and their disease management. OBJECTIVE We aimed to describe the development and implementation process of a web-based, animated patient education tool that supports oncology patients remotely in the context of fewer in-person interactions with health care providers. METHODS The platform created presents multilingual oncology care instructions. Animations concerning cancer care and mental health during the COVID-19 pandemic as well as immunotherapy and chemotherapy guides were the major areas of focus and represented 6 final produced video guides. RESULTS The videos were watched 1244 times in a period of 6 months. The most watched animation was the COVID-19 & Oncology guide (viewed 565 times), followed by the video concerning general treatment orientations (viewed 249 times) and the video titled “Chemotherapy” (viewed 205 times). Although viewers were equally distributed among the age groups, most were aged 25 to 34 years (342/1244, 27.5%) and were females (745/1244, 59.9%). CONCLUSIONS The implementation of a patient education platform can be designed to prepare patients and their caregivers for their treatment and thus improve outcomes and satisfaction by using a methodical and collaborative approach. Multimedia tools allow a portion of a patient’s care to occur in a home setting, thereby freeing them from the need for hospital resources.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.075 | 0.013 |
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".