“We’re already doing this work”: ethical research with community-based organizations
Bibliographic record
Abstract
BACKGROUND: Public health research frequently relies on collaborations with community-based organizations, and these partnerships can be essential to the success of a project. However, while public health ethics and oversight policies have historically focused on ensuring that individual subjects are protected from unethical or unfair practices, there are few guidelines to protect the organizations which facilitate relationships with - and are frequently composed of - these same vulnerable populations. As universities, governments, and donors place a renewed emphasis on the need for community engaged research to address systematic drivers of health inequity, it is vital that the ways in which research is conducted does not uphold the same intersecting systems of gender, race, and class oppression which led to the very same health inequities of interest. METHODS: To understand how traditional notions of public health research ethics might be expanded to encompass partnerships with organizations as well as individuals, we conducted qualitative interviews with 39 staff members (executive directors and frontline) at community-based organizations that primarily serve people who use drugs, Black men who have sex with men, and sex workers across the United States from January 2016 - August 2017. We also conducted 11 in-depth interviews with professional academic researchers with experience partnering with CBOs that serve similar populations. Transcripts were analyzed thematically using emergent codes and a priori codes derived from the Belmont Report. RESULTS: The concepts of respect, beneficence, and justice are a starting point for collaboration with CBOs, but participants deepened them beyond traditional regulatory concepts to consider the ethics of relationships, care, and solidarity. These concepts could and should apply to the treatment of organizations that participate in research just as they apply to individual human subjects, although their implementation will differ when applied to CBOs vs individual human subjects. CONCLUSIONS: Academic-CBO partnerships are likely to be more successful for both academics and CBOs if academic researchers work to center individual-level relationship building that is mutually respectful and grounded in cultural humility. More support from academic institutions and ethical oversight entities can enable more ethically grounded relationships between academic researchers, academic institutions, and community based organizations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.395 | 0.344 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.010 |
| Science and technology studies | 0.017 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.035 |
| Insufficient payload (model declined to judge) | 0.058 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".