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Record W3081505995 · doi:10.1080/13561820.2020.1807481

Interprofessional education and collaborative practice research during the COVID-19 pandemic: Considerations to advance the field

2020· article· en· W3081505995 on OpenAlexaff
Kelly Lackie, Ghaidaa Najjar, Alla El‐Awaisi, Jody S. Frost, Sylvia Langlois, Dean Lising, Andrea Pfeifle, H. Marshall Ward, Andreas Xyrichis, Hossein Khalili

Bibliographic record

VenueJournal of Interprofessional Care · 2020
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsWestern UniversityDalhousie UniversityUniversity of TorontoAcadia UniversityNova Scotia Health AuthorityHealth Canada
Fundersnot available
KeywordsInterprofessional educationPharmacyPharmacistMedical educationPandemicCoronavirus disease 2019 (COVID-19)MedicineSociologyLibrary scienceNursingHealth carePolitical science

Abstract

fetched live from OpenAlex

In the past few months, we have heard repeatedly, “these are unprecedented times”. Truer words may have never been spoken for we find ourselves amid a global pandemic that has created exceptional, ...

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)0.417
metaresearch head score (Gemma)0.333
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.417
Threshold uncertainty score0.718

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4170.333
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0090.012
Science and technology studies0.0300.066
Scholarly communication0.0490.091
Open science0.0110.036
Research integrity0.0500.051
Insufficient payload (model declined to judge)0.0120.004

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 head0.083
GPT teacher head0.562
Teacher spread0.480 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations55
Published2020
Admission routes1
Has abstractyes

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