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Record W4312010454 · doi:10.5281/zenodo.7459339

Living with the pandemic in Quebec – MAVIPAN : Methodological report - General Information (English)

2020· report· en· W4312010454 on OpenAlexaffabout
Patrick Blouin, Annie LeBlanc

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2020
Typereport
Languageen
FieldHealth Professions
TopicHealthcare Systems and Practices
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPandemicLibrary scienceGeographyData scienceCoronavirus disease 2019 (COVID-19)Computer scienceMedicine

Abstract

fetched live from OpenAlex

The Living with the pandemic (MAVIPAN - Ma vie avec la pandémie) study was launched as a collective effort brought together by the 4 research centers of the CIUSSS de la Capitale-Nationale, namely CIRRIS Center for Interdisciplinary Research in Rehabilitation and Social Integration, CRUJeF University Research Center for Youth and Families, CERVO research center, and VITAM Centre de recherche en santé durable, with the support of Université Laval’s PULSAR collaborative research platform. It is intended to be an initiative designed to support the research community and serve as a public health information tool. This methodological report "General Information" describes the principal methodological elements extracted from the research protocol of the My Life and the Pandemic (MAVIPAN) study: Study design and population, sampling, organization of data collection, ethical certification. More information is available in other documents in the MAVIPAN library. If you have any questions or concerns regarding the information in this document, we suggest you contact the study’s team at mavipan.ciussscn@ssss.gouv.qc.ca

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)0.023
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.050
Threshold uncertainty score0.366

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.030
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0070.011
Science and technology studies0.0050.002
Scholarly communication0.0030.001
Open science0.0030.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0170.002

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 head0.310
GPT teacher head0.456
Teacher spread0.146 · 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 designNot applicable
Domainnot available
GenreMethods

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

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