Skin cancer on Instagram: implications for adolescents and young adults
Bibliographic record
Abstract
Given the popularity and reach of Instagram among American adolescents and young adults (AYA), the well-known influence of social media on the behaviors of youth, and the rising rates of melanoma in this age group, this study sought to examine and describe the content of a sample of Instagram posts related to skin cancer. At three different times, a search of Instagram was conducted using #skincancer as the hashtag. Descriptive analyses of Instagram characteristics and content was performed. Overall, content focused on prevention (33.3%), skin cancer treatment (29.3%) and preventive measures such as using sunscreen and protective gear (29.3%). Nearly one-quarter discussed the ABCDEs (Asymmetrical; Borders are irregular; Color is not even; Diameter is large; Evolving) of screening and detection. Instagram postings that covered skin cancer prevention (n = 50, 33.3%) more often discussed the role of sun exposure in the development of skin cancer (28.0% vs. 10.0%, p = 0.005) and use of sunscreen and protective gear (62.0% vs. 13.0%, p < 0.001). The findings of this study indicate that a considerable portion of the Instagram posts included in this study focused on prevention. Thus, indicating that Instagram could be used to promote health, particularly among AYA.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".