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Record W2397898304 · doi:10.1525/jer.2013.8.1.55

Ethical Challenges and Opportunities for Nurses in HIV and AIDS Community-Based Participatory Research in Jamaica

2013· article· en· W2397898304 on OpenAlexafffund
Colleen Davison, Eulalia Kahwa, Nancy Edwards, Uki Atkinson, Susan Roelofs, Cerese Hepburn-Brown, Joyette Aiken, Pauline Dawkins, Tania Rae, Denise MacFarlane

Bibliographic record

VenueJournal of Empirical Research on Human Research Ethics · 2013
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsUniversity of OttawaQueen's University
FundersCanadian Institutes of Health Research
KeywordsAutonomyCommunity-based participatory researchParticipatory action researchStakeholderPublic relationsCommunity engagementResearch ethicsSociologyNursingStakeholder engagementSolidaritySustainabilityHealth careMedicinePolitical sciencePolitics

Abstract

fetched live from OpenAlex

As part of a multinational program of research, we undertook a community-based participatory research project in Jamaica to strengthen nurses' engagement in HIV and AIDS policy. Three leadership hubs were purposefully convened and included small groups of people (6-10) from diverse HIV and AIDS stakeholder groups in Jamaica: frontline nurses and nurse managers in primary and secondary care settings; researchers; health care decision makers; and other community members. People living with HIV or AIDS were among the hub members. Using a relational public health ethics framework, we outline some of the ethical challenges and opportunities experienced by the research team and the leadership hubs. Data included research assistant field notes and hub progress reports. Emerging ethical concerns were associated with relational personhood, social justice, relational autonomy, relational solidarity, and sustainability of the hub activities.

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
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativelow
gptMetaresearch
Domain: Methods · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativemedium
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)0.195
metaresearch head score (Gemma)0.044
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesMetaresearch, Science and technology studies, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1950.044
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.001
Science and technology studies0.0050.004
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0020.060
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 head0.977
GPT teacher head0.758
Teacher spread0.219 · 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

Citations14
Published2013
Admission routes2
Has abstractyes

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