Empowering patients through social media: The benefits and challenges
Bibliographic record
Abstract
This article explores the range of social media platforms used by patients and examines the benefits and challenges of using these tools from a patient perspective. A literature review was performed to investigate the use of social media technology by patients. The MEDLINE database was searched using the terms "social media" and "patient." The search was conducted in September 2012 and yielded 765 abstracts. Initially, 63 abstracts were selected. All articles dating from 2004 through 2012 were included. Only 12 articles were found to be relevant for the purposes of the review. The results of this research found that there appears to be an increase in the use of social media by patients across the healthcare spectrum. The research indicates a promising future for the use of social media by patients; however, evidence related to the efficacy and effectiveness of social media is currently limited. Various challenges have also been identified relating to privacy and security concerns, usability, the manipulation of identity, and misinformation. The use of social media technology is an emerging trend for patients who are seeking health information. Conclusions are that such technology holds promise for improving patient engagement and empowerment and community building. Social media has a future in healthcare, especially with regard to patient engagement and empowerment; however, there are several challenges to overcome before the technology can achieve its potential.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".