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Impact of stress, distress and feelings of indebtedness on adherence to immunosuppressants following kidney transplantation

2006· article· en· W1972668358 on OpenAlexafffund
Marie Achille, Amélie Ouellette, Stéphanie Fournier, Mélanie Vachon, Marie‐Josée Hébert

Bibliographic record

VenueClinical Transplantation · 2006
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsHôpital Notre-DameUniversité de MontréalMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMedicineDistressFeelingKidney transplantationTransplantationIntensive care medicineInternal medicineClinical psychologySocial psychology

Abstract

fetched live from OpenAlex

In order to ensure transplantation's long-term success, transplant recipients need to comply with a strict regimen of immunosuppressant medication on a daily basis for the rest of their lives. Nonadherence is one of the major causes of organ rejection. Because compliance is voluntary, it is likely to be influenced by an individual's beliefs and feelings. This study examined the impact on compliance of the following factors: (1) transplant-related stress; (2) general perceived stress; (3) psychosocial distress and (4) feelings of indebtedness and guilt towards the donor. Fifty kidney recipients (34 men, 16 women) filled out self-report questionnaires. The results indicate that 46% acknowledged sub-optimal compliance in the last month; patients more often reported not taking the medication exactly as prescribed than forgetting to take it. The results also suggest that psychological distress and general perceived stress affect compliance negatively, whereas feelings of indebtedness improve it. These results have implications for the understanding and management of compliance following organ transplantation.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.391
Teacher spread0.351 · 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 source (direct Gemma or distilled Codex), 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

Citations90
Published2006
Admission routes2
Has abstractyes

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