Enhancing rigor and justice in genetic counseling research with reflexivity and positionality
Bibliographic record
Abstract
In the rapidly evolving landscape of genetic counseling research, acknowledging the dynamic interplay between the researchers and the research is critical. Positionality, which encompasses how researchers' social, cultural, and professional identities intersect with their work, along with reflexive practices that promote self-awareness, emerge as essential tools in promoting transparency, rigor, and ethical integrity in research. In this article, we explore the importance of both positionality and reflexivity in genetic counseling research. By highlighting the impact of researchers' perspectives on study design, data interpretation, and knowledge translation, we seek to underscore how positionality and reflexivity can be used to confront biases and power imbalances in the research process. Our discussion extends to both qualitative and quantitative methodologies, showcasing the role of reflexive and positionality statements in enhancing research credibility, inclusivity, and justice. We have provided actionable guidance and reflective questions for constructing robust positionality statements and documenting reflexivity across research phases. Reflexive research practices may advance justice-oriented evidence that informs clinical practices, research, and the genetic counseling profession more broadly. At the same time, we must balance the need to disclose privilege and biases with the imperative to protect marginalized individuals from potential exploitation and harm, ensuring these disclosures do not reinforce existing power imbalances nor compromise safety and autonomy. By advancing reflexivity and positionality, this article advocates for a justice-centered approach to genetic counseling research, ensuring a more representative and ethically responsible body of knowledge.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.654 | 0.635 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.015 | 0.136 |
| Scholarly communication | 0.036 | 0.040 |
| Open science | 0.006 | 0.035 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier 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".