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Record W4280557847 · doi:10.2217/rme-2022-0019

Roles and Responsibilities in Stem Cell Research: A Focus Group Study with Stem Cell Researchers and Patients

2022· article· en· W4280557847 on OpenAlexfundno aff
Lars Assen, Karin Jongsma, Rosario Isasi, Marianna A. Tryfonidou, Annelien L. Bredenoord

Bibliographic record

VenueRegenerative Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsnot available
FundersHorizon 2020 Framework ProgrammeEuropean CommissionArthritis SocietyDutch Arthritis Society
KeywordsFocus groupStem cellPublic relationsTranslational researchEngineering ethicsMedicinePolitical scienceSociologyBiologyEngineeringPathology

Abstract

fetched live from OpenAlex

Background: The perspectives of researchers and patients regarding roles and responsibilities in stem cell research are rarely studied, but these could offer insights about responsible research conduct. Method: We have conducted a qualitative study consisting of focus groups with both early- (n = 7) and late-career stem cell researchers (n = 11) that are primarily based in Europe, and with Dutch patients with chronic lower back pain (n = 9). These focus groups have been analyzed thematically. Results: Four themes were identified: 1) roles and responsibilities in the laboratory, 2) responsibilities of and toward patients and the public, 3) the role of regulation and 4) structural hurdles for responsibility. Discussion: The results suggest that responsible research conduct could be improved by addressing grant application procedures, publication pressure and by providing support of dissemination activities for researchers. Conclusion: Responsibility in stem cell research could be enhanced by embracing open science initiatives and targeted training.

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)0.048
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.255

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0480.042
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0120.010
Scholarly communication0.0040.006
Open science0.0020.007
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.128
GPT teacher head0.364
Teacher spread0.236 · 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 designQualitative
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

Citations7
Published2022
Admission routes1
Has abstractyes

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