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Record W3048152072 · doi:10.1097/tp.0000000000003401

Living Kidney Donors’ Financial Expenses and Mental Health

2020· article· en· W3048152072 on OpenAlexaffabout
Lianne Barnieh, Jennifer Arnold, Neil Boudville, Meaghan S. Cuerden, Mary Amanda Dew, Christine Dipchand, Liane S. Feldman, John S. Gill, Martin Karpinski, Scott Klarenbach, Greg Knoll, Charmaine E. Lok, Matt Miller, Mauricio Monroy, Chris Nguan, G. V. Ramesh Prasad, Jessica M. Sontrop, Leroy Storsley, Amit X. Garg

Bibliographic record

VenueTransplantation · 2020
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsSt. Michael's HospitalInstitute for Clinical Evaluative SciencesMcGill University Health CentreUniversity of OttawaUniversity of AlbertaFoothills Medical CentreUniversity of ManitobaSt. Joseph’s Healthcare HamiltonUniversity of British ColumbiaWestern UniversityUniversity of TorontoDalhousie UniversityMcMaster UniversityLondon Health Sciences Centre
Fundersnot available
KeywordsMental healthDonationPsychosocialMedicineBeck Depression InventoryAnxietyQuality of life (healthcare)Depression (economics)PsychiatryNursing

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.558
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.264
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2020
Admission routes2
Has abstractyes

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