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Record W3136637621 · doi:10.1016/j.pmedr.2021.101356

Prevalence of tanning equipment use among Canadians

2021· article· en· W3136637621 on OpenAlexaffabout
Sami S. Qutob, James P. McNamee, Orly Brion

Bibliographic record

VenuePreventive Medicine Reports · 2021
Typearticle
Languageen
FieldMedicine
TopicSkin Protection and Aging
Canadian institutionsHealth Canada
Fundersnot available
KeywordsEnvironmental healthMedicineDemographySalon

Abstract

fetched live from OpenAlex

The objective of this study was to collect prevalence estimates of indoor tanning usage and associated injuries in Canada. The rapid response component of the 2019 Canadian Community Health Survey collected data on the use of tanning equipment in the previous 12 months, including reasons for use, frequency/duration of use, precautions taken and adverse reactions or injuries. The 2019 research findings were as follows, an estimated 3.0% (95% CI: 2.5-3.4%) of Canadians reported that they had used indoor tanning equipment in the past year. Among users, 71.1% (95% CI: 63.9-78.3%) were female and females aged 18-34 were significantly more prevalent users compared to females aged 45 or older. The prevalence of indoor tanning was higher among people without a university degree; however, there were no differences in prevalence by household income or region. Most users indicated they used indoor tanning equipment within a tanning salon (75.3%: 95% CI: 69.1-81.6%) and the most common reason for usage was to develop a "protective" base tan (72.1%: 95% CI: 65.2-78.9%). Over one third (39.2%: 95% CI: 31.1-47.2%) of all users reported 10 or more sessions in the past year. The prevalence of indoor UV tanning usage is declining in Canada. Similar to results in 2014, the majority of users continue to be female, with a large number in the 18 to 34 age group.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.027
GPT teacher head0.298
Teacher spread0.271 · 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.

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

Citations5
Published2021
Admission routes2
Has abstractyes

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