Understanding How and Under What Circumstances Social Media Supports Health Care Providers' Knowledge Use in Clinical Practice: A Realist Review
Bibliographic record
Abstract
Background: Although theoretical frameworks exist to guide social media interventions, few of them make it explicit how social media is supposed to work to improve the knowledge use by health care providers. This study aimed to synthesize literature to understand how and under what circumstances social media supports knowledge use by health care providers in clinical practice. Methods: We followed the realist review methodology described by Pawson et al. It involved six iterative steps: (1) develop an initial program theory; (2) search for evidence; (3) select and appraise studies; (4) extract data; (5) synthesize data; and (6) draw conclusions. Results: Of the 7,175 citations retrieved, 32 documents were prioritized for synthesis. We identified two causal explanations of how social media could support health care providers' knowledge use, each underpinned by distinct context-mechanism-outcome (CMO) configurations. We defined these causal explanations as: (1) the rationality-driven approach that primarily uses open social media platforms (n = 8 CMOs) such as Twitter, and (2) the relationality-driven approach that primarily uses closed social media platforms (n = 6 CMOs) such as an online community of practice. Key mechanisms of the rationality-driven approach included social media content developers capabilities and capacities, in addition to recipients' access to, perceptions of, engagement with, and intentions to use the messages, and ability to function autonomously within their full scope of practice. However, the relationality-driven approach encompassed platform receptivity, a sense of common goals, belonging, trust and ownership, accessibility to expertise, and the fulfillment of needs as key mechanisms. Conclusion: Social media has the potential to support knowledge use by health care providers. Future research is necessary to refine the two causal explanations and investigate their potential synergistic effects on practice change.
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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 | 0.043 | 0.209 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| 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".