Good practices in harnessing social media for scholarly discourse, knowledge translation, and education
Bibliographic record
Abstract
INTRODUCTION: There still remains a gap between those who conduct science and those who engage in educating others about health sciences through various forms of social media. Few empirical studies have sought to define useful practices for engaging in social media for academic use in the health professions. Given the increasing importance of these platforms, we sought to define good practices and potential pitfalls with help of those respected for their work in this new field. METHODS: We conducted a qualitative study, guided by constructivist grounded theory principles, of 17 emerging experts in the field of academic social media. We engaged in a snowball sampling technique and conducted a series of semi-structured interviews. The analytic team consisted of a diverse group of researchers with a range of experience in social media. RESULTS: Understanding the strengths of various platforms was deemed to be of critical importance across all the participants. Key to building online engagement were the following: 1) Culture-building strategies; 2) Tailoring the message; 3) Responsiveness; and 4) Heeding rules of online engagement. Several points of caution were noted within our participants' interviews. These were grouped into caveat emptor and the need for critical appraisal, and common pitfalls when broadcasting one's self. DISCUSSION: Our participants were able to share a number of key practices that are central to developing and sharing educational content via social media. The findings from the study may guide future practitioners seeking to enter the space. These good practices support professionals for effective engagement and knowledge translation without being harmed.
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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 | 0.210 | 0.190 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.017 | 0.061 |
| Scholarly communication | 0.032 | 0.037 |
| Open science | 0.005 | 0.034 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".