Effects of using WeChat/WhatsApp on physical and psychosocial health outcomes among oncology patients: A systematic review
Bibliographic record
Abstract
The purpose of this systematic review is to summarize the potential effects of the WeChat and WhatsApp mobile applications in cancer management. This systematic review was written in accordance with PRISMA guidelines. CINAHL, PubMed, ProQuest Nursing and Allied Health Database, PsycINFO, PsycARTICLES, and ERIC were utilized for the literature search. Articles were included if they evaluated the outcomes of using WeChat/WhatsApp for cancer management, and excluded if they were qualitative studies, not published in peer-reviewed journals, protocols for a future study, or conference abstracts. 20 studies were included in this systematic review, with a total sample of 3110 participants. Interventions were utilized to share educational information with participants, follow-up after surgical operations, and in clinical communication. Outcomes, including pain, medication adherence, self-efficacy, quality of life, and depression, were statistically significantly improved in the WeChat/WhatsApp intervention groups in comparison to the control groups or to baseline measurements of the study participants. Outcomes of sleep and rehospitalization rate were improved without reaching statistical significance. Outcomes of anxiety, fatigue, and adverse drug reactions were found to be conflictive among included studies. This systematic review suggested that use of WeChat/WhatsApp on cancer management might improve various physical and psychosocial health outcomes among oncological patients. Limitations of the study include solely reviewing English language articles published in academic journals and most of the studies being from one country. Future research should be conducted in various countries among diverse communities, including rural areas, to ascertain the effects of WeChat/WhatsApp in different populations.
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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".