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Kidney Transplant Education using the Explore Transplant Ontario Package

2018· article· en· W2883132220 on OpenAlexaffabout
Dmitri Belenko, Candice Richardson, V. Gupta, Evan Tang, Nathaniel Edwards, Márta Novák, John Devin Peipert, Amy D. Waterman, István Mucsi

Bibliographic record

VenueTransplantation · 2018
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsToronto General Hospital
Fundersnot available
KeywordsMedicinePsychological interventionInterimDialysisRandomized controlled trialKidney transplantationTransplantationIntervention (counseling)Patient educationPhysical therapyKidney diseaseKidney transplantInternal medicineFamily medicineNursing

Abstract

fetched live from OpenAlex

Introduction Improving transplant education for patients with end stage kidney disease (ESKD) may improve their access to kidney transplantation (KT). Non-Caucasian patients with ESKD are less likely to receive KT compared to Caucasian patients, and may benefit more from such interventions. Explore Transplant Ontario (ETO) is a KT education tool, utilizing informational brochures alongside patient and living donor testimonial videos to motivate patients and potential donors to consider KT and donation. In a pilot study, we assessed the impact of ETO use on KT knowledge, and investigated ethnical disparities between groups. Materials and Methods In this non-randomized, parallel arm controlled study, 230 in-centre dialysis patients were recruited from two hospital units. Patients in the intervention arm (n=124) received ETO packages and regular follow-up to address KT-related questions; control group received education as usual. Patients across both sites completed questionnaires at baseline, 6 and 9 months afterwards, with interim follow-up visits. KT knowledge was assessed with a 19-item true/false & multiple-choice questionnaire, scored 0-19, with higher scores indicating higher KT knowledge. sssResults and Discussion Compared to the control group, patients in the intervention group were significantly older (mean age = 63 [±10] vs 55 [±14], p<0.001), more likely African Canadian, and less likely to have completed Grade 12. Baseline knowledge scores were different, with a higher score for control group (mean [SD] = 8.21 [3.7] vs 6.77 [3.4]). In the intervention arm, 66 patients watched the videos and/or read the brochures, while 58 patients only read the brochures or didn't use the package at all. Knowledge scores increased at 6- and 9-month follow-up for both groups, with significantly higher increase for the intervention group compared to control (mean increase at 6 months [SD] =1.92 [2.7] vs 0.79 [2.7], p=0.01; at 9 months=1.73 [3.3] vs 0.67 [3.0], p=0.04). The follow-up knowledge score was higher in those who watched the videos in addition to reading, compared to those who only used the package partially (mean score [SD] at 6 months = 9.5 [3.0] vs 8.1 [3.4], p=0.03; at 9 months =9.4 [3.1] vs 7.9 [2.9], p=0.04). Baseline knowledge scores tended to be lower for non-Caucasians within the study population (mean [SD] scores for Caucasians=8.26[4.0], Asians=7.6[3.6], Blacks=7.1[3.4], p=0.14). At study end, the change in knowledge score was not different between ethnic groups (mean increase [SD]: Caucasians=1.7[3.7], Asians=1.4[3.3], Blacks=1.7[3.2], p=0.907).Conclusion Transplant education using the ETO program lead to significantly higher increase in KT knowledge compared to care as usual. Non-Caucasian patients may be at risk for lower transplant baseline KT knowledge, and may benefit from more culturally tailored educational interventions. Watching the videos appears to confer the greatest educational benefit. Further research is needed to assess the impact of ETO use on KT rates, and to develop tailored interventions to address ethnic disparities.

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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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0340.001

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 head0.041
GPT teacher head0.297
Teacher spread0.256 · 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 designNot applicable
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".

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Citations3
Published2018
Admission routes2
Has abstractyes

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