Developing an Analytic Tool Using a Participatory Research Approach to Examine Gender Differences in the Living Kidney Donation Process
Bibliographic record
Abstract
Background: Sex-disaggregated data demonstrate that there are more female than male living kidney donors (LKDs), and this is widely thought to be a manifestation of gender inequities. A better understanding of how gender norms, roles, and relations influence the living kidney donation process is needed. Objective: We aimed to develop an analytical tool that can be used to conduct a systematic gender analysis of the living kidney donation process. Design: A participatory research approach was used. Setting: Canada. Participants: Canadian living kidney donors and health care professionals. Methods: Using a participatory research approach, we co-designed a gender analysis matrix (GAM) applicable to the context of living kidney donation. This tool is the first step into conducting a gender analysis of the living kidney donation process. Participants included 11 healthcare professionals, 6 LKDs, 1 patient partner, and a core methodology team (3 qualitative researchers and a gender analysis expert). Results: Following an iterative process, a final LKD-GAM with 4 columns and 4 rows was created. The gender-specific domains are access to resources, division of labor and everyday practices, social norms and beliefs, and decision-making power and autonomy. Context-specific domains are the decision to donate, physical and mental wellbeing, experiences with health services, and broader social and economic impacts of (non-)donation. Key insights for our future work include diversity in our sample (include LKD candidates, men, and different life stages) and unpacking the notions of consent, constrained consent, and subtle coercion. Limitations: The GAM's scope is likely limited to the Canadian context. The study was also limited by the recruitment of LKDs from a past list of participants and by the lack of cultural and linguistic diversity in the sample. Conclusion: Using a participatory co-design approach, we have developed a robust tool that will inform a multinational qualitative study to better understand factors contributing to gender disparities in living kidney donation.
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
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 | 0.118 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".