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Record W3005826528 · doi:10.1177/1556264620902657

The Concept of Vulnerability in Mental Health Research: A Mixed Methods Study on Researcher Perspectives

2020· article· en· W3005826528 on OpenAlexafffund
Corinne Lajoie, J Poleksić, Dearbhail Bracken‐Roche, Mary Ellen Macdonald, Éric Racine

Bibliographic record

VenueJournal of Empirical Research on Human Research Ethics · 2020
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsMcGill UniversityWestern UniversityUniversité de MontréalMcGill University Health CentreMontreal Clinical Research Institute
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health Research
KeywordsVulnerability (computing)WarrantMental healthResearch ethicsNormativeHarmAutonomyPerspective (graphical)PsychologyPluralism (philosophy)SociologySocial psychologyEngineering ethicsEpistemologyPolitical sciencePsychotherapistPsychiatryLaw

Abstract

fetched live from OpenAlex

The concept of vulnerability plays a central role in research ethics in signaling that certain research participants warrant more careful consideration because their risk of harm is heightened due to their participation in research. Despite scholarly debates, the descriptive and normative meanings ascribed to the concept have remained disengaged from the perspective of users of the concept and those concerned by its use. In this study, we report a survey- and interview-based investigation of mental health researcher perspectives on vulnerability. We found that autonomy-based understandings of vulnerability were predominant but that other understandings coexisted, reflecting considerable pluralism. A wide range of challenges were associated with this concept, and further training was recommended by researchers.

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
Qualitativehigh
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)0.612
metaresearch head score (Gemma)0.722
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesMetaresearch, Science and technology studies, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.454
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.6120.722
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.008
Science and technology studies0.0040.017
Scholarly communication0.0000.000
Open science0.0040.003
Research integrity0.0010.123
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 head0.975
GPT teacher head0.836
Teacher spread0.140 · 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

Citations34
Published2020
Admission routes2
Has abstractyes

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