Transplanting Change: The Slow Progress of Gender Equity in Editorial Leadership
Bibliographic record
Abstract
Purpose: To understand the infl uence of several factors on potential donors' willingness to be evaluated for living donation, and how they diff er across demographic characteristics (race/ethnicity, age, and sex). Methods:We analyze survey data fi elded to relatives of kidney disease patients recruited through an online survey panel, capturing realistic potential living kidney donors regardless of donation behaviors.The percentage reporting each reason making them more likely or less likely to donate, overall and by demographic characteristics, will be reported.The association of these reasons with concrete living donation behaviors will also be tested.Results: Reasons related to the respondent's relationship (76.3% salient) with the patient and the respondent's expectation of a successful or worthwhile process (likelihood of matching [67.9%] and own current health status [71.3%]) were the most salient, meaning they were tied to increased or decreased willingness.In contrast, fi nancial reasons showed low salience (3).We fi nd substantial diff erences in the reactions to specifi c reasons by racial/ethnic identifi cation, age group, and sex.Finally, belonging to a salient category (more or less likely) was associated with the count of actions taken to pursue kidney donation for only certain reasons, indicating that consideration of some reasons (particularly an individual's own health status) may have a stronger association with actual LDKT pursuit.Conclusions: While fi nancial and religious reasons have been the focus of federal policy and existing literature, respectively, we fi nd that these reasons have comparatively low salience.In contrast, potential donors' relationship with the patient are much more salient, alongside practical considerations such as their health and likelihood of matching.Demographic diff erences in these responses suggest that a one-size-fi ts-all approach to discussing specifi c reasons for donation -especially fi nancial impacts -is likely to be met with varying degrees of receptiveness.
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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 | 0.046 | 0.106 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.021 | 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; 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".