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Record W2888310719 · doi:10.1016/j.pmedr.2018.08.010

Availability of tanning salons in Ontario relative to indoor tanning policy (2001–2017)

2018· article· en· W2888310719 on OpenAlexafffundabout
Jennifer E. McWhirter, Spencer Byl, Alyssa Green, William Sears, Andrew Papadopoulos

Bibliographic record

VenuePreventive Medicine Reports · 2018
Typearticle
Languageen
FieldMedicine
TopicSkin Protection and Aging
Canadian institutionsUniversity of Guelph
FundersGovernment of OntarioUniversity of Guelph
KeywordsSalonInternational agencyEnvironmental healthSkin cancerLegislationAgency (philosophy)Indoor airPublic healthMedicineBusinessCancerPolitical scienceEnvironmental scienceNursingSociologyEnvironmental engineeringLaw

Abstract

fetched live from OpenAlex

(SCPA). Tanning salon listings were obtained from the 2001 to 2017 editions of InfoCanada's Ontario Business to Business Sales and Marketing directories. Using descriptive statistics and regression analysis, we assessed the number of tanning salons before and after: 1) the 2006 International Agency for Research on Cancer (IARC) report on indoor tanning and skin cancer; 2) the 2009 World Health Organization (WHO) reclassification of artificial UV radiation as carcinogenic; and 3) the passing and enactment of Ontario's SCPA in 2013 and 2014, respectively. There were fewer tanning salon listings in the years after vs. before the IARC report, the WHO reclassification, and the passing and enactment of the SCPA. The number of tanning salons in Ontario, Canada has been declining since 2006, which may reflect a decline in indoor tanning bed use. Key public health policy instruments, including legislation and public education, appear to be associated with this trend, suggesting they may contribute to deterring indoor tanning.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.057
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.044
GPT teacher head0.352
Teacher spread0.308 · 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

Citations7
Published2018
Admission routes3
Has abstractyes

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