Emojis and Emoticons in Health Care and Dermatology Communication: Narrative Review
Bibliographic record
Abstract
Background: Emoticons and emojis have become staple additions to modern-day communication. These graphical icons are now embedded in daily society through the various forms of popular social media and through users' personal electronic conversations. With ever-increasing use and inclusivity, exploration of the possible health care and dermatology applications of these tools is imperative. Objective: The goal of this narrative review was to provide and evaluate an up-to-date literature survey examining the utility of emoticons and emojis in medicine. Special attention was paid to their existing and potential uses in the field of dermatology, especially during the COVID-19 pandemic. Methods: A PubMed search of peer-reviewed publications was performed in mid-2021 to collect articles with emoticon or emoji keywords in combination with other health care-relevant or dermatology-relevant keywords. Screening of publications and described studies was performed by the authors with education and research experience in health care, dermatology, social media, and electronic communication trends. Selected articles were grouped based on common subjects for qualitative analysis and presentation for in-depth discussion. Results: From this extensive search, researchers were able to identify a wide variety of publications detailing the use of emoticons and emojis in general health care, pediatric health care, public health, and dermatology. Key subject areas that emerged from the investigation included the ability of emoticons and emojis to improve communication within pediatric health care, enhance mood and psychological assessment or mental health screening in adults, develop interventions to improve patient medication adherence, complement novel means of public health and COVID-19 surveillance, and bolster dermatology-specific applications. Conclusions: This review illuminated the repurposing of emojis and emoticons for a myriad of advantageous functions in health care and public health, with applications studied in many populations and situations. Dermatology-specific uses were relatively sparse in the literature, highlighting potential opportunities for growth in future studies and practices. The importance of diversity and inclusivity has extended to emojis, with the recent introduction of skin color customization and new emojis better representing the comprehensive spectrum of users' experiences. A continuously evolving and technology-driven population creates a unique niche for emoticons and emojis to ease worldwide communication and understanding, transcending the barriers of age, language, and background. We encourage future studies and innovations to better understand and expand their utility.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".