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Record W4387540452 · doi:10.1080/0194262x.2023.2242430

Measuring the Uptake of Pharmacoepidemiologic Research Through Qualitative Analysis of Citations: A Case Study from a Canadian Network of Researchers

2023· article· en· W4387540452 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueScience & Technology Libraries · 2023
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsMcGill UniversityKellogg's (Canada)Jewish General HospitalDalhousie University
Fundersnot available
KeywordsSalaryLibrary scienceContext (archaeology)BibliometricsScopusSociologyPolitical sciencePsychologyMEDLINEComputer scienceLawHistory

Abstract

fetched live from OpenAlex

Health research, including drug safety research, is communicated, in part, by being cited; understanding these citations can help determine its reach and impact. We analyzed the uptake of a Canadian Network for Observational Drug Effect Studies study of the heart failure risk of incretin-based drugs using quantitative and qualitative bibliometric approaches. A Scopus® search (2016–2020) returned 127 citing articles, mostly single studies and review articles. Many were also high impact journals, with intended audiences of other researchers, policy makers, and practitioners. Using the Becker Model, 93% contributed to “advancing knowledge.” Research impact can be difficult to establish. We have demonstrated one comprehensive approach that can be further adapted and automated; researchers and funders need to determine the best indicators, tools, and frameworks to use that are feasible and context relevant.

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.

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)0.010
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.767
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.026
Science and technology studies0.0000.006
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.703
GPT teacher head0.594
Teacher spread0.109 · 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