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Record W2884192941 · doi:10.1055/s-0038-1644968

Modernizing the Regulation of Self-Care Products in Canada

2018· article· en· W2884192941 on OpenAlexaffabout
A.E.A. Moir

Bibliographic record

VenuePlanta Medica International Open · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicHealthcare innovation and challenges
Canadian institutionsHealth Canada
Fundersnot available
KeywordsBusinessProduct (mathematics)Health careMedical prescriptionMarketingPrescription drugPublic relationsRisk analysis (engineering)MedicineNursingPolitical scienceEconomic growthEconomics

Abstract

fetched live from OpenAlex

Health Canada is looking to modernize its approach to regulating self-care products. These are everyday products available for purchase without a prescription and include cosmetics, natural health products and non-prescription drugs. The goals of this regulatory modernization are to align the levels of oversight with risk to the consumer, and to help consumers make better informed decisions. While many self-care products are low risk, the regulatory oversight for each category is very different, including requirements for product labelling. To ensure that the new approach is informed by a broad range of perspectives, Health Canada has consulted extensively with Canadians, including consumer groups, academia, health professionals and industry. Health Canada is taking a phased approach, with plans to introduce key regulatory changes over the next two years. First, in the fall of 2018, Health Canada will introduce targeted amendments to the Natural Health Products Regulations to improve labelling of natural health products, including a facts table. These changes are intended to better support consumers in selecting and safely using a product. Second, in early 2019, Health Canada will introduce targeted amendments to the Food and Drug Regulations to align regulatory oversight of non-prescription drugs with the risk of the product, including the creation of expedited pathways for lower-risk products. The goal is to focus oversight where it will most benefit Canadians, based on evidence and the risk of the product. While these first two projects are underway, Health Canada will continue to develop plans for further improvements to how self-care products are regulated, which will follow over the coming years.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.813
Threshold uncertainty score0.483

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.075
GPT teacher head0.360
Teacher spread0.286 · 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 designNot applicable
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

Citations0
Published2018
Admission routes2
Has abstractyes

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