Associations Between Emotions, Social Media Use, and Sun Exposure Among Young Women: Ecological Momentary Assessment Study
Bibliographic record
Abstract
Background Research has pointed to a connection between social media use, emotions, and tanning behaviors. However, less is known about the role specific emotions may play in influencing social media use and how emotions and social media use may each be associated with outdoor tanning. Objective This paper aims to examine the connection between emotions, social media use, and outdoor tanning behaviors among young women, a group particularly important for skin cancer prevention efforts. Methods We used ecological momentary assessment to collect data from 197 women aged 18 to 25 years 3 times a day for 7 days in July 2018. We collected data from women in 2 states. Results We found that boredom was associated with increased time spent on social media and that increased time spent on social media was associated with increased time spent outdoors without sun protection. Conclusions Our results highlight that social media may be a particularly important channel for skin cancer prevention efforts targeting young women, as more social media use was associated with increased time spent outdoors with skin exposed. Researchers should consider the role of emotions in motivating social media use and subsequent tanning behaviors. Additionally, as boredom was associated with social media use, intervention developers would benefit from developing digital and social media interventions that entertain as well as educate.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".