Identifying Social Media Competencies for Health Professionals: An International Modified Delphi Study to Determine Consensus for Curricular Design
Bibliographic record
Abstract
STUDY OBJECTIVE: The use of social media by health professionals is widespread. However, there is a lack of training to support the effective use of these novel platforms that account for the nuances of an effective health and research communication. We sought to identify the competencies needed by health care professionals to develop an effective social media presence as a medical professional, with the goal of building a social media curriculum. METHODS: We conducted a modified Delphi study, utilizing Kraiger's Knowledge, Skills, and Attitudes framework to identify appropriate items for inclusion in a social media curriculum targeted at health care professionals. Experts in this space were defined as health care professionals who had delivered workshops, published papers, or developed prominent social media tools/accounts. They were recruited through a multimodal campaign to complete a series of 3 survey rounds designed to build consensus. In keeping with prior studies, a threshold of 80% endorsement was used for inclusion in the final list of items. RESULTS: Ninety-eight participants met the expert criteria and were invited to participate in the study. Of the 98 participants, 92 (94%) experts completed the first round; of the 92 experts who completed the first round, 83 (90%) completed the second round; and of the 83 experts who completed the second round, 81 (98%) completed the third round of the Delphi study. Eighteen new items were suggested in the first survey and incorporated into the study. A total of 46 items met the 80% inclusion threshold. CONCLUSION: We identified 46 items that were believed to be important for health care professionals using social media. This list should inform the development of curricular activities and objectives.
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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 | 0.097 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".