MétaCan
Menu
← Back to cohort
Record W3016792206 · doi:10.1101/2020.04.14.20065631

Lack of coordination and medical disinformation in Canadian self-assessment tools for COVID-19

2020· preprint· en· W3016792206 on OpenAlexaffabout
Brieanne Olibris, Amir Attaran

Bibliographic record

VenuemedRxiv · 2020
Typepreprint
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsWilfrid Laurier UniversityUniversity of Ottawa
Fundersnot available
KeywordsDisinformationTriagePandemicCoronavirus disease 2019 (COVID-19)Consistency (knowledge bases)OddsQuarantineIsolation (microbiology)Public healthMedicinePsychologyBusinessMedical emergencyNursingPolitical scienceComputer scienceSocial mediaDisease

Abstract

fetched live from OpenAlex

Abstract As SARS-CoV-2 threatens to overwhelm health systems in Canada, it is imperative that provinces are able to plan and manage an effective and reduced risk response. For this response to be most effective, it must reflect an evidence-based, pan-Canadian response. We designed four different prototypical patients with a combination of common COVID-19 symptoms and opportunities for exposure who were made to self-assess using the 10 provincial COVID-19 self-assessment tools on 1 April. These tools were developed to allow individuals to self-triage, allowing health systems direct capacity to testing and care. We assessed the consistency of the self-assessment tools and of the guidance provided to the patients. While the tools generally screen in three areas, the scope of included COVID-19 associated symptoms as well as the opportunities for exposure, and therefore transmission, vary between provinces such that no two provinces screened in the same way. This was, in turn, reflected in the inconsistency in guidance found. A patient with cough who had travelled abroad or had close contact with a confirmed case within 14 days received the most consistent guidance, with remaining patients receiving guidance ranging from mandatory quarantine or self-isolation to being told they did not have COVID-19 symptoms, guidance at odds with medical evidence. Thus, there is not a single, evidence-based Canadian standard of care simply for self-assessment. Without consistency in public health guidance, Canadians cannot appropriately self-isolate to mitigate community transmission, nor can the necessary valid and reliable data be collected to inform critical epidemiological models that help guide pandemic response. If federal and provincial governments are unable to coordinate a response, Parliament must use its available jurisdiction to legislate a duty on both to follow national standards, so as to improve coordination on COVID-19 in coming months.

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)0.036
metaresearch head score (Gemma)0.135
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.299

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.135
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.004
Science and technology studies0.0040.002
Scholarly communication0.0040.001
Open science0.0030.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.099
GPT teacher head0.433
Teacher spread0.334 · 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 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

Citations0
Published2020
Admission routes2
Has abstractyes

Explore more

Same venuemedRxiv→Same topicViral Infections and Outbreaks Research→French-language works237,207→