Exploring the psychological construct of resilience in kidney transplantation: A scoping review
Bibliographic record
Abstract
BACKGROUND: Extensive literature has highlighted the psychological burden experienced by kidney transplant recipients (KTRs) and its association with adverse outcomes. Psychological resilience can serve as a measure of baseline vulnerability, and low resilience is associated with poor mental health. We aimed to synthesize the existing literature that has explored the concept of resilience in kidney transplantation. METHODS: A scoping review was conducted due to the anticipated heterogeneity of the literature. Any empirical study that measured resilience using a validated tool in KTRs was included. Resilience could be a variable, a predictor, or an outcome. All study designs were considered with no time restrictions. RESULTS: Of the 4525 titles and abstracts screened, 14 were eligible for inclusion. Sample sizes ranged from 10 to 505 KTRs. One study exclusively focused on developing and validating a resilience scale while others used existing tools. Three studies compared resilience between different populations and the results were heterogeneous: similar resilience between KTRs and dialysis/pre-KT patients (n = 2) and another reporting better resilience in KTRs (n = 1). A decline in resilience scores after pediatric-adult transition (n = 1) and 3 months post-transplant (n = 1) was reported. In terms of outcomes, higher resilience was associated with medication adherence (n = 1), lower frailty (n = 2), and lower risk of psychopathology (n = 2). Two of the three included studies reported improvements in resilience scores with an exercise program and a resilience-enhancing program. CONCLUSIONS: Our review highlights that resilience is an underused and poorly explored construct in KTRs. We recommend explorative and interventional work as resilience is measurable and modifiable.
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 | 0.009 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| 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".