Depression in cancer: An evaluation of patient education resources.
Bibliographic record
Abstract
e24194 Background: In recent years, there has been increasing awareness surrounding mental health and depression among cancer patients. Concurrently, the internet has cemented its role as a mainstay source of health information for the general public. However, little is known about the quality of online resources addressing depression specifically in cancer patients. Therefore, we aim to systematically evaluate the quality of such information. Methods: The term "depression in cancer patients" was searched online using the search engine Google and the meta-search engines Dogpile and Yippy. A set of predetermined inclusion and exclusion criteria was applied to all search results, which yielded 48 websites for inclusion. An evidence-based rating tool was then used to score the websites based on the six domains of Affiliation, Accountability, Interactivity, Structure & Organization, Readability, and Content Quality. The results were analyzed using descriptive statistics. Results: Of the 48 websites evaluated, 50% were commercial. In terms of accountability measures, 63% of websites disclosed authorship, 54% cited one or more reliable sources, and 38% were updated within the last two years. Although in-site search engines and video support were found in 94% and 52% of websites respectively, the presence of other interactive features were considerably lower. The average readability was at a grade 12.3 level using the Flesch-Kincaid scale and 11.3 using the SMOG Index, both of which were significantly higher than the traditionally recommended grade-six level ( p < 0.0001 for both). The most commonly covered topics were symptoms and treatment – found on 87% and 83% of websites respectively. Prevention and prognosis were not covered by any of the websites. Content accuracy was generally high among covered topics. Conclusions: Many websites addressing depression in cancer have poor authorship disclosure, attribution, and currency. Additional interactive features should be encouraged to facilitate user-friendliness. Poor readability may pose a barrier for patient comprehension, indicating a need for health care providers to proactively guide patients to suitable resources. Despite high content accuracy in other topics, prevention and prognosis are seldom covered. Our results could help guide the development of new patient education materials and better inform health care providers about the limitations of available online resources. Future research should aim to elucidate reasons contributing to difficult readability levels and identify topics that patients need additional information in.
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".