Social media use among patients and caregivers: a scoping review
Bibliographic record
Abstract
OBJECTIVE: To map the state of the existing literature evaluating the use of social media in patient and caregiver populations. DESIGN: Scoping review. DATA SOURCES: Medline, CENTRAL, ERIC, PubMed, CINAHL Plus Full Text, Academic Search Complete, Alt Health Watch, Health Source, Communication and Mass Media Complete, Web of Knowledge and ProQuest (2000-2012). STUDY SELECTION: Studies reporting primary research on the use of social media (collaborative projects, blogs/microblogs, content communities, social networking sites, virtual worlds) by patients or caregivers. DATA EXTRACTION: Two reviewers screened studies for eligibility; one reviewer extracted data from relevant studies and a second performed verification for accuracy and completeness on a 10% sample. Data were analysed to describe which social media tools are being used, by whom, for what purpose and how they are being evaluated. RESULTS: Two hundred eighty-four studies were included. Discussion forums were highly prevalent and constitute 66.6% of the sample. Social networking sites (14.8%) and blogs/microblogs (14.1%) were the next most commonly used tools. The intended purpose of the tool was to facilitate self-care in 77.1% of studies. While there were clusters of studies that focused on similar conditions (eg, lifestyle/weight loss (12.7%), cancer (11.3%)), there were no patterns in the objectives or tools used. A large proportion of the studies were descriptive (42.3%); however, there were also 48 (16.9%) randomised controlled trials (RCTs). Among the RCTs, 35.4% reported statistically significant results favouring the social media intervention being evaluated; however, 72.9% presented positive conclusions regarding the use of social media. CONCLUSIONS: There is an extensive body of literature examining the use of social media in patient and caregiver populations. Much of this work is descriptive; however, with such widespread use, evaluations of effectiveness are required. In studies that have examined effectiveness, positive conclusions are often reported, despite non-significant findings.
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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 | 0.027 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.028 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".