‘Just Google it’—A scoping review of online mental health resources for survivors of breast cancer
Bibliographic record
Abstract
OBJECTIVE: As the Internet is a ubiquitous resource for information, we aimed to replicate a patient's Google search to identify and assess the quality of online mental health/wellbeing materials available to support women living with or beyond cancer. METHODS: A Google search was performed using a key term search strategy including search strings 'cancer', 'wellbeing', 'distress' and 'resources' to identify online resources of diverse formats (i.e., factsheet, website, program, course, video, webinar, e-book, podcast). The quality evaluation scoring tool (QUEST) was used to analyse the quality of health information provided. RESULTS: The search strategy resulted in 283 resources, 117 of which met inclusion criteria across four countries: Australia, USA, UK, and Canada. Websites and factsheets were primarily retrieved. The average QUEST score was 10.04 (highest possible score is 28), indicating low quality, with 92.31% of resources lacking references to sources of information. CONCLUSIONS: Our data indicated a lack of evidence-based support resources and engaging information available online for people living with or beyond cancer. The majority of online resources were non-specific to breast cancer and lacked authorship and attribution.
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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 | 0.017 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.029 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".