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Record W2808196163 · doi:10.17269/s41997-018-0080-3

Contested roles of Canada’s Chief Medical Officers of Health

2018· article· en· W2808196163 on OpenAlexafffundvenueabout
Patrick Fafard, Brittany McNena, Agatha Suszek, Steven J. Hoffman

Bibliographic record

VenueCanadian Journal of Public Health · 2018
Typearticle
Languageen
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsMcMaster UniversityYork UniversityUniversity of OttawaGlobal Affairs Canada
FundersCanadian Institutes of Health ResearchNorges ForskningsrådGovernment of Ontario
KeywordsStatuteMandateLegislationGovernment (linguistics)ConfusionPublic healthPublic administrationPolitical sciencePublic relationsPower (physics)LawMedicinePsychology

Abstract

fetched live from OpenAlex

The roles and responsibilities of Canada's Chief Medical Officers of Health (CMOHs) are contested. On the one hand, they are senior public servants who confidentially advise government on public health matters and manage the implementation of government priorities. On the other hand, CMOHs are perceived as independent communicators and advocates for public health. This article analyzes public health legislation across Canada that governs the CMOH role. Our legal analysis reveals that the presence and degree of advisory, communication, and management roles for the CMOH vary considerably across the country. In many jurisdictions, the power and authority of the CMOH is not clearly defined in legislation. This creates great potential for confusion and conflict, particularly with respect to CMOHs' authority to act as public health advocates. We call on governments to clarify their preferences when it comes to the CMOH role and either amend the relevant statute or otherwise find ways to clarify the mandate of their CMOHs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.031
metaresearch head score (Gemma)0.067
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.868
Threshold uncertainty score0.955

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.067
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0310.017
Scholarly communication0.0130.003
Open science0.0030.005
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0060.001

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 head0.104
GPT teacher head0.431
Teacher spread0.327 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations22
Published2018
Admission routes4
Has abstractyes

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