Depression in cancer: quality assessment of online patient education resources
Bibliographic record
Abstract
OBJECTIVE: Psychiatric comorbidities are common among cancer patients. However, little is known about the quality of online information regarding these conditions. This study uses a validated tool to systematically determine the strengths and limitations of websites addressing depression in cancer patients. METHODS: The term "depression in cancer patients" was searched online using the search engines Google, Yippy, and Dogpile. A set of predetermined inclusion/exclusion criteria was applied to all search results, which yielded 48 websites for inclusion. A validated rating tool was 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. 63% of websites identified authorship, 54% cited reliable sources, 37% provided links, and 38% were updated within the last 2 years. 94% of websites featured a search engine and 60% had at least four structural tools. Average readability was at a grade 12.3 level using the Flesch-Kincaid scale and 11.3 using the Simple Measure of Gobbledygook Index. The most completely and accurately covered topics of depression were symptoms and treatment-83% and 73% respectively. Its prevention and prognosis were not covered by any of the websites. CONCLUSIONS: A validated rating tool was applied to evaluate the quality of online information for depression in cancer patients. Website accountability was poor, readability was often at a level that is too difficult for the lay audience, and the topics of prevention and prognosis were seldom covered.
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 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 | 0.016 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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 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".