Systemic Factors Contributing to Gender Differences in Living Kidney Donation: A Systematic Review and Meta-Synthesis Using the Social-Ecological Model Lens
Bibliographic record
Abstract
INTRODUCTION: The field of living kidney donation is profoundly gendered contributing to a predominance of women, mothers, and wives as living kidney donors (LKDs). Individual factors have traditionally been emphasized, and there is a limited appreciation of relational, community, and sociocultural influences in decision-making. We aimed to comprehensively capture existing evidence to examine the relative importance of these factors. METHODS: This was a systematic review of existing literature that has explored the motivation of different genders to become LKDs. Of the 3,188 records screened, 16 studies from 13 counties were included. Data were synthesized thematically using the Social-Ecological Model lens. RESULTS: At the individual level, themes related to intrinsic motivation; thoughtful deliberation; and attitudes, fears, and beliefs; however, evidence demonstrating differences between men and women was minimal. Greater variation between genders emerged along the relational (coercion from family/network, relationship with the intended recipient, self-sacrifice within the family unit, and stability/acceptance within family); community (economic value and geographic proximity to recipient); and sociocultural (gendered societal roles, social norms and beliefs, social privilege, and legislation and policy) dimensions. The relative importance of each factor varied by context; cultural components were inferred in each study, and economic considerations seemed to transcend the gender divide. CONCLUSIONS: A complex interplay of factors at relational, community, and sociocultural levels influences gender roles, relations, and norms and manifests as gender disparities in living kidney donation. Our findings suggest that to address gender disparities in living donation, dismantling of deep-rooted systemic contributors to gender inequities is needed.
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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 | 0.025 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".