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Record W4406195046 · doi:10.1186/s12961-024-01282-9

Investigating the trustworthiness of research evidence used to inform public health policy: a qualitative interview study on the use of predatory journal citations in policy documents

2025· article· en· W4406195046 on OpenAlexafffund
Marc A. Albert, Manoj M. Lalu, Agnes Grudniewicz

Bibliographic record

VenueHealth Research Policy and Systems · 2025
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsWilfrid Laurier UniversityOttawa HospitalUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of CanadaTelfer School of Management, University of OttawaUniversity of Ottawa
KeywordsHealth services researchPublic healthTrustworthinessHealth administrationHealth policyQualitative researchPeer reviewHealth economicsPublic health policySocial policyPublic policyMedicinePolitical sciencePsychologyNursingSociologySocial psychologySocial scienceLaw

Abstract

fetched live from OpenAlex

Evidence-based policymaking has increased policymakers' capacity to make scientifically informed health policy decisions. However, reaping the benefits of this approach requires avoiding untrustworthy research - potential sources of which are predatory journals. In this study, we sought to understand how research cited in policy documents is sourced and evaluated, and identify factors that may be contributing to the citation of predatory journals or other less trustworthy evidence. To this end, we conducted semi-structured interviews with individuals who have prepared public health policy documents. These interviews were thematically analysed, and five key overarching themes were generated regarding the process of deciding how to develop policy documents (e.g. which individuals to involve) and how this may impact which information is included; obstacles such as limited evidence that may hinder policy document development; and concerns around transparency throughout the development process. Our findings highlight that in many cases, information cited in policy documents is sourced and evaluated with variable rigour. This may contribute to the citation of untrustworthy research in policy documents. Certain steps can be taken to help minimize any potential negative impact of relying on such sources (e.g. improving transparency), but a better understanding of policymakers' perspectives regarding how taking these steps would impact their decision-making process may be required to ensure successful implementation.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearchBibliometricsResearch integrity
Domain: Evaluation · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativelow
gptResearch integrity
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativelow
models splitAgreement compares identical category sets and study designs across arms.

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)0.452
metaresearch head score (Gemma)0.607
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Science and technology studies, Scholarly communication
Consensus categoriesMetaresearch, Bibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.382
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.4520.607
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.1130.313
Science and technology studies0.0020.001
Scholarly communication0.0040.001
Open science0.0040.002
Research integrity0.0000.002
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 head0.979
GPT teacher head0.789
Teacher spread0.190 · 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

Labeled directly by 2 models reading the full record.

MetaresearchBibliometricsResearch integrity

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designQualitative
DomainEvaluation
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

Citations4
Published2025
Admission routes2
Has abstractyes

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