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Record W2970034591 · doi:10.1177/1556264619872366

Recognizing the Role of Research Assistants in the Protection of Participants in Vulnerable Circumstances

2019· article· en· W2970034591 on OpenAlexafffund
Judith Friedland, Elizabeth Peter

Bibliographic record

VenueJournal of Empirical Research on Human Research Ethics · 2019
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsPublic Health OntarioUniversity of Toronto
FundersUniversity of Toronto
KeywordsPrivilege (computing)NegotiationVulnerability (computing)Principal (computer security)Variety (cybernetics)Qualitative researchFocus groupResearch ethicsPower (physics)Moral imperativeEngineering ethicsPsychologyPolitical sciencePublic relationsSociologyLaw

Abstract

fetched live from OpenAlex

Little is known about how research assistants (RAs) protect participants in vulnerable circumstances. Using a critical qualitative method informed by feminist ethics, we ran five focus groups with experienced RAs. We identified two themes: (a) expressing moral competencies (subthemes: recognizing power, privilege, and vulnerability; adapting processes and providing support; understanding the sources of moral competencies) and (b) negotiating and making transparent roles and responsibilities (subthemes: separating responsibilities as a clinician from those of an RA; critically reflecting on the shared responsibilities of principal investigators and RAs; and identifying the role of the Research Ethics Committee). Although RAs possess a variety of moral competencies and have an important role in protecting research participants in vulnerable circumstances, that role is largely unrecognized.

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
gemmaMetaresearch
Domain: Methods · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativelow
gptno category
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)0.483
metaresearch head score (Gemma)0.391
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.483
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.4830.391
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.008
Science and technology studies0.0010.005
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0010.076
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 head0.949
GPT teacher head0.763
Teacher spread0.186 · 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.

Metaresearch

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

Study designQualitative
DomainMethods
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

Citations1
Published2019
Admission routes2
Has abstractyes

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