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Record W2043780734 · doi:10.1300/j010v46n03_03

Living Kidney Donors' Experiences with the Health Care System

2008· article· en· W2043780734 on OpenAlexaff
Judith Belle Brown, Mary Lou Karley, Neil Boudville, Ruth Bullas, Amit X. Garg, Norman Muirhead

Bibliographic record

VenueSocial Work in Health Care · 2008
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsLondon Health Sciences CentreThe King's UniversityWestern University
Fundersnot available
KeywordsMedicineNursingHealth carePolitical science

Abstract

fetched live from OpenAlex

The purpose of this study was to explore living kidney donors' perceived experiences with the health care system from the period prior to being tested as a potential donor, through to post donation discharge and follow-up. Qualitative methodology, using a phenomenological approach, explored the experiences, feelings, and ideas of 12 purposefully selected living kidney donors' interface with the health care system. Eight men and four women were interviewed four to 29 years post donation. Interviews were audio taped and transcribed verbatim. An iterative and interpretive analysis was conducted. Themes emerging from the data included factors influencing living kidney donors' decision to be tested as potential donors, the importance of emotional support, and humanistic care. This in turn impacted on their experience of: (1) the role of information in the decision-making process; (2) their tolerance of issues related to hospitalization and; (3) their perception of the quality of care. The findings of this study provide suggestions for the role of social work and improvement in the health care system to better address the needs of living kidney donors.

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)0.010
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0100.007
Scholarly communication0.0070.004
Open science0.0010.010
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.014
GPT teacher head0.291
Teacher spread0.277 · 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 designQualitative
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

Citations18
Published2008
Admission routes1
Has abstractyes

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