MétaCan
Menu
Back to cohort
Record W4311861892 · doi:10.3390/curroncol29120759

Association of Sun Safety Behaviors and Barriers with Sunburn History in College Students in a Region with High UV Exposure

2022· article· en· W4311861892 on OpenAlexvenueno aff
Dylan T. Miller, Zoe Baccam, Robin B. Harris

Bibliographic record

VenueCurrent Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicSkin Protection and Aging
Canadian institutionsnot available
Fundersnot available
KeywordsSunburnMedicineSun protectionSkin cancerSun exposureDermatologyEnvironmental healthCancerDemographyInternal medicine

Abstract

fetched live from OpenAlex

Over five million cases of skin cancer are diagnosed each year in the United States with melanoma the third most common cancer in young adults. While publications have shown that sunburns increase the risk of developing melanoma throughout the lifetime including in adolescence and adulthood showing the importance of altering sun exposing behaviors throughout the lifetime, use of sun protection in college students remails low. In Fall 2019, an online survey of undergraduate students living on campus at a large southwestern university was conducted to determine the frequency of recent sunburns as well as sun protective behaviors and perceived knowledge of and barriers to sun protection. Associations between knowledge, behaviors, and barriers with self-reported sunburn were evaluated using logistic regression. Over 46% of 458 students reported at least one sunburn in the past three months and 21% reported having multiple sunburns in that period. Furthermore, 53% reported that they intentionally tanned their skin outdoors occasionally or more frequently, while 6.4% reported using an indoor tanning bed occasionally or more. Adjusted for skin sensitivity, recent sunburn history was associated with higher tanning activity scores and with high agreement that tanning was attractive (p < 0.01). This information can inform a more targeted series of intervention programming on the university campus.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.756

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.320
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueCurrent OncologySame topicSkin Protection and AgingFrench-language works237,207