Tailoring information for adults over 50 living with cancer in the age of social media: A systematic review
Bibliographic record
Abstract
BACKGROUND: Cancer often requires patients to make swift, informed, treatment decisions. Despite their engagement with healthcare providers and digital resources, cancer patients over 50 often experience high levels of unmet information needs during these critical times. However, there is a lack of evidence-based information on their supportive-care information needs. OBJECTIVE: To examine cancer patients' (aged 50 +) information and health literacy needs and their motivations for using social media (i.e Twitter/X, Facebook, YouTube and Instagram) during decision-making. METHODS: A systematic literature review, following the PRISMA guidelines, was conducted using electronic databases (Scopus, Web of Science and PubMed/MEDLINE) and grey literature. All original articles published from January 2002 to October 2023 were extracted and analysed within COVIDENCE and NVIVO14 for themes following narrative and tabular analysis. Risk of bias was assessed using the Newcastle-Ottawa Quality Assessment scale. RESULTS: Of 761 articles identified, six were included. Patients' health literacy was determined to be moderate to low. At decision-making points, cancer patients over 50 needed personalised, supportive and disease-related information. They preferred holistically tailored information and were satisfied with how their doctors met their needs. Complimentary therapies and dietary recommendations were well received by patients of Chinese, Vietnamese, and Australian backgrounds. Patients over 50 accessed social media throughout their cancer. Although useful for obtaining support and information, social media raised patients' concern around misinformation. CONCLUSION: Our findings highlight the importance of meeting the information needs of cancer patients over 50 and incorporating a holistic approach to information delivery. Social media sites targeting consumers can be useful tools for healthcare institutions to supply accurate, user-friendly information. TRIAL REGISTRATION: PROSPERO registration number - CRD42022358710.
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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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".