Tips and Pitfalls in Using Social Media Platforms for Survey Dissemination
Bibliographic record
Abstract
IntroductionSocial media has become a prevalent platform for survey dissemination, despite the paucity of literature on this topic. The purpose of this paper is to outline the benefits and drawbacks of and best practices for social media-based surveys.MethodsWe performed a scoping review of this topic and explored different strategies commonly employed for conducting efficient health care surveys via social media platforms.ResultsThe main advantages of social media-based surveys are the convenience and flexibility of survey design, their relatively low cost, the anonymity of responders, and the ability to reach a broader population of responders across geographical boundaries. Several measures can be adopted to avoid issues inherent in this approach, such as data disruption and response duplication, as well as to enhance ethical behaviors and consent compliance. We discuss limitations associated with unclear distribution of survey respondents and outline survey fraud as a major impediment to the online propagation of surveys on various social media platforms.DiscussionThe use of social media to disseminate surveys on various medical specialty topics has garnered global participation, particularly during the COVID-19 pandemic. Ethical codes of conduct emphasize the need for professionalism and truthfulness, and disclosure of potential conflicts of interest on the part of respondents, and high-quality survey research on the part of researchers.ConclusionWe advocate for the novel use of social media to promote large and diverse health care surveys. Additional studies should further explore the use of emerging social media platforms for survey dissemination and their impact on health care research.
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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 | 0.655 | 0.836 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.025 | 0.048 |
| Open science | 0.011 | 0.020 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".