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Record W2734932564 · doi:10.1353/nib.2017.0027

Developing Trust, Multiple Identities, and Participatory Research: Select Examples

2017· article· en· W2734932564 on OpenAlexaboutno aff
Scarlett E. Hopkins

Bibliographic record

VenueNarrative Inquiry in Bioethics · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsnot available
Fundersnot available
KeywordsCommunity-based participatory researchParticipatory action researchContext (archaeology)PhotovoicePublic relationsSociologyHealth equityCommunity engagementCitizen journalismMedicinePolitical sciencePublic healthGeographyNursingAnthropology

Abstract

fetched live from OpenAlex

Developing Trust, Multiple Identities, and Participatory Research: Select Examples Scarlett Hopkins In community–based participatory research (CBPR), trust among participating individuals and groups is paramount to achieving goals. Trust is an ever–changing and evolving concept implying increasing willingness to take risks with potentially delicate information, an assumption that promises will be kept, and a belief in the good will of the other. When outside researchers enter a community, they are greeted by community members and organizations who have often had prior experience with outside researchers. A critical aspect of this process is the value of outside researchers developing what clinical psychologist James Kelly called an ‘eco–identity” or a way of becoming known in the local community that helps community members “locate” the researcher in the community context and provides a basis for relationship development and subsequent trust. In this paper, I will describe the development of trusting relationships involving genetic studies with Alaska Native communities. The Center for Alaska Native Health Research (CANHR) located at the University of Alaska Fairbanks was funded in 2001 by the National Institutes of Health (NIH) as a Center of Biomedical Research Excellence (COBRE) focusing on obesity and cardiometabolic disease in Yup’ik Alaska Native people in rural southwestern Alaska. Initially, it was comprised of three multi-disciplinary research studies including genetics of obesity, nutrition and physical activity, and cultural understandings of health. Now, CANHR includes behavioral health and nutritional research as well as epigenomic and pharmacogenomic research to address health disparities in Alaska Native people. As a Registered Nurse, I am interested in health disparities in Alaska Native people and the influence of culture on health beliefs, behavior and outcomes. I began working with CANHR in 2002, first as a graduate student in cultural anthropology, then as a research coordinator, and now leading the Community Engagement and Clinical Support Core. One of my responsibilities as a graduate student was initial contact with potential communities who had been selected by the Yukon–Kuskokwim Health Corporation (YKHC), the native health entity providing medical services and overseeing health research in this region. These initial contacts included several visits to the communities to meet with Tribal Governments and community members to discuss the research and gain approval. These first visits were the beginning of trusting and sustained partnerships spanning a 15–year period with over 1,800 Yup’ik participants in our genetic studies. It is important to reflect on and document how trust develops in community research, particularly when it involves basic science rather than community interventions intending to have direct community impact. In the 15 years that I have been involved in multiple projects involving multiple communities, my primary role has been to facilitate the relationship between the university research project team members while simultaneously being involved in recruitment of participants, data collection and dissemination of research progress and findings. During these years, I have come to understand how my various identities have played a role in allowing and promoting relationships with community members. These identities involve my [End Page E12] southern upbringing, being a mid–aged woman with a family, a clinical background in nursing, and my role as a researcher. Initially, I was very nervous, but also very excited about my first visits to these communities. I was not sure what to expect, how I would be received, and what I should or should not do to be culturally respectful. I had read much of the ethnographic literature about Yup’ik people to prepare myself. However, I felt the best and most genuine approach was to just be myself. Being brought up in the South, I was taught to be friendly and greet everyone, whether I knew him or her or not. In each Yup’ik community, I would smile and nod or say hello when passing. I found Yup’ik people were very friendly and welcoming to me. When appropriate, I would introduce myself, and share why I was in their community. I think my excitement over being in their community and my eagerness to learn about their culture was evident. Over time, I got to know many individuals in the communities by name, learned much abut their lives and their...

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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: Commentary
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 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)0.056
metaresearch head score (Gemma)0.074
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.056
Threshold uncertainty score0.296

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0560.074
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0080.018
Scholarly communication0.0080.022
Open science0.0020.010
Research integrity0.0080.010
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 head0.968
GPT teacher head0.764
Teacher spread0.204 · 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 · Commentary

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

Citations0
Published2017
Admission routes1
Has abstractyes

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