Improving Patient Understanding and Outcomes in Lung Cancer Using an Animated Patient’s Guide with Visual Formats of Learning
Bibliographic record
Abstract
Lung cancer patient education resources that address barriers to health literacy, improve understanding, and demonstrate improved patient outcomes are limited. Our study aim was to evaluate and report on learner knowledge improvement and intent to implement behavior change, and validate the benefits of the You and Lung Cancer website and YouTube resources. Our study occurred from November 2017 to December 2023. We evaluated audience reach (visit sessions, unique visitors, country origins, page views) and calculated top views by media type (animations, expert videos, patient videos). We assessed the impact and commitment to change through learner surveys (areas of interest, intention to modify behaviors, and intention to discuss disease management with providers) and tested the knowledge of learners pre- and post-reviewing of website content. Our program reached 794,203 views globally; 467,546 were unique visitors; and 243,124 (51%) were unique visitors from the USA. Of US visitors, 46% identified as lung cancer patients. These were patients in treatment (38%), survivors (8%), family members or caregivers (21%), and healthcare providers (14%) with other audiences unspecified (19%). Three areas of highest learner importance were the animations "Understanding Non-Small Cell Lung Cancer" (180,591), "Staging of Lung Cancer" (144,238), and "Treatment and Management of Small Cell Lung Cancer" (49,244). Our study confirmed areas of importance to lung cancer patients and suggests that visual formats of learning, such as animations, can mitigate health literacy barriers and help improve patient understanding and outcomes. Exporting this format of learning to other cancers has the potential to benefit patients and improve health outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".