Navigating Your Social Media Presence: Opportunities and Challenges
Bibliographic record
Abstract
Social media use is on the rise. With a 10-fold increase in use over the last decade, it is estimated that over 69% of adults now use social media on a regular basis. Social media has been identified as a key resource for health professionals, including psychologists, to learn new knowledge, interact with others, keep up-to-date on the latest research, and get tips on how to integrate evidence-based information into their clinical practice. The objectives of this article are to (a) summarize professional opportunities in the area of social media and outline the various ways that pediatric psychologists can use social media in their research, practice, and advocacy; and (b) provide practical suggestions for pediatric psychologists on creating, sharing and interacting over social media. Recommendations for participating in activities such as live tweeting, video streaming, and social media evaluation are discussed. Common barriers, potential pitfalls, and ethical issues associated with use of social media by pediatric psychologists are also addressed. Implications for Impact Statement This article offers an overview of social media applications for pediatric psychologists engaged in research and clinical practice. Suggestions for using social media, including ethical and practical considerations, are also reviewed.
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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.011 | 0.021 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".