Psychosocial Characteristics and Satisfaction with Healthcare Following Transplantation
Bibliographic record
Abstract
Abstract Background To live with an organ transplant is to live with aspects of acute and chronic illness, including fear, uncertainty, change, and frequent contact with the healthcare system. Previous research highlighted the problem that patients who live with a chronic condition such as transplantation are very dissatisfi ed with their healthcare. The two goals of the present study were: (1) to identify the level of satisfaction with healthcare experienced by patients with type 1 diabetes who had received either a single kidney or a pancreas and kidney transplant; and (2) to identify sociodemographic, medical, and psychosocial characteristics related to satisfaction with healthcare in this population. Methods Forty‐seven patients with type 1 diabetes recruited at Notre‐Dame Hospital who had received either a kidney (n = 22) or a pancreas‐kidney (n = 25) transplant completed a mail survey. A cluster analysis was performed in order to determine the levels and profi les of satisfaction of the patients. Results Descriptive analyses indicated that patients seemed to have a fairly good overall level of satisfaction with healthcare. Cluster analysis yield two profi les of satisfaction among the patients: a profi le of high satisfaction and a profi le of moderate satisfaction. High satisfaction with healthcare was associated with better psychosocial adjustment and less psychological distress. Conclusions Satisfaction with healthcare following transplantation by patients with type 1 diabetes seems to be associated with psychosocial adjustment and psychological status. Even if these results need to be verifi ed with a larger sample, they might encourage healthcare practitioners to pay special attention to patients whose satisfaction with healthcare is low because this attitude seems to be part of patients' global psychosocial status.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".