Developing Reporting Guidelines for Social Media Research (RESOME) by Using a Modified Delphi Method: Protocol for Guideline Development
Bibliographic record
Abstract
BACKGROUND: Social media platforms, such as Facebook, Twitter, and Instagram, are being increasingly used to deliver public health interventions. Despite the high level of research interest, there is no consensus or guidance on how to report on social media interventions. Reporting guidelines that incorporate elements from behavior change theories and social media engagement frameworks could foster more robust evaluations that capture outcomes that have an impact on behavior change and engagement. OBJECTIVE: The aim of this project is to develop, publish, and promote a list of items for our Reporting Guidelines for Social Media Research (RESOME) checklist. METHODS: RESOME will be developed by using a modified Delphi approach wherein 2 rounds of questionnaires will be sent to experts and stakeholders. The questionnaires will ask them to rate their agreement with a series of statements until a level of consensus is reached. This will be followed by a web-based consensus meeting to finalize the reporting guidelines. After the consensus meeting, the reporting guidelines will be published in the form of a paper outlining the need for the new guidelines and how the guidelines were developed, along with the finalized checklist for reporting. Prior to publication, the guidelines will be piloted to check for understanding and simplify the language used, if necessary. RESULTS: The first draft of RESOME has been developed. Round 1 of the Delphi survey took place between July and December 2021. Round 2 is due to take place in February 2022, and the web-based consensus meeting will be scheduled for the spring of 2022. CONCLUSIONS: Developing RESOME has the potential to contribute to improved reporting, and such guidelines will make it easier to assess the effectiveness of social media interventions. Future work will be needed to evaluate our guidelines' usefulness and practicality. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/31739.
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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 | 0.320 | 0.465 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.028 | 0.012 |
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".