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Record W2913206286 · doi:10.1177/1203475418825114

A Qualitative Analysis of Canadian Indoor Tanning Policies

2019· article· en· W2913206286 on OpenAlexaffabout
Pavandeep Gill, Sunil Kalia

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2019
Typearticle
Languageen
FieldMedicine
TopicSkin Protection and Aging
Canadian institutionsUniversity of British ColumbiaVancouver Coastal HealthUniversity of Calgary
Fundersnot available
KeywordsEnvironmental healthMedicineSunbathingSignageSkin cancerInternational agencyAgency (philosophy)PopulationBusinessAdvertisingCancer

Abstract

fetched live from OpenAlex

BACKGROUND: The International Agency for Research on Cancer classifies artificial tanning devices as Group 1 human carcinogens. Studies have shown that use of indoor tanning before age 35 can increase the risk of melanoma development by 75%. It has therefore been recommended that indoor tanning use be restricted in individuals younger than age 18. OBJECTIVES: This study aims to review the state of provincial indoor tanning policies, especially in regards to use by youth across Canada, and what strategies are being implemented to enforce them. METHODS: Focused interviews were conducted with representatives from the provincial Ministries of Health across Canada in May and June 2014. Follow-up interviews were performed between February and May 2017. RESULTS: As of January 2018, regulations are in effect in all Canadian provinces restricting indoor tanning by minors and requiring display of signage warning of the risks of indoor tanning by salons. However, there are discrepancies among the provinces on how and if tanning salons are monitored and how and if these regulations are enforced. CONCLUSIONS: While implementing youth bans on indoor tanning is a promising start, all Canadian provinces need to ensure that efforts are being undertaken to ensure compliance with these policies to effectively combat the rising incidence of skin cancer among the Canadian population.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.488
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.001
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 head0.054
GPT teacher head0.358
Teacher spread0.304 · 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

Citations3
Published2019
Admission routes2
Has abstractyes

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