Living Kidney Donors’ Financial Expenses and Mental Health
Bibliographic record
Abstract
BACKGROUND: Living kidney donors incur donation-related expenses, but how these expenses impact postdonation mental health is unknown. METHODS: In this prospective cohort study, the association between mental health and donor-incurred expenses (both out-of-pocket costs and lost wages) was examined in 821 people who donated a kidney at one of the 12 transplant centers in Canada between 2009 and 2014. Mental health was measured by the RAND Short Form-36 Health Survey along with Beck Anxiety Inventory and Beck Depression Inventory. RESULTS: A total of 209 donors (25%) reported expenses of >5500 Canadian dollars. Compared with donors who incurred lower expenses, those who incurred higher expenses demonstrated significantly worse mental health-related quality of life 3 months after donation, with a trend towards worse anxiety and depression, after controlling for predonation mental health-related quality of life and other risk factors for psychological distress. Between-group differences for donors with lower and higher expenses on these measures were no longer significant 12 months after donation. CONCLUSIONS: Living kidney donor transplant programs should ensure that adequate psychosocial support is available to all donors who need it, based on known and unknown risk factors. Efforts to minimize donor-incurred expenses and to better support the mental well-being of donors need to continue. Further research is needed to investigate the effect of donor reimbursement programs, which mitigate donor expenses, on postdonation mental health.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".