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P11.19: System-Level Barriers to Living Donor Kidney Transplantation: A Cross-Sectional Survey Study of Health Professionals

2022· article· en· W4296340883 on OpenAlexaffabout
Shaifali Sandal, Ian Schiller, Nandini Dendukuri, Jorane‐Tiana Robert, Khaled Katergi, Ahsan Alam, Marcelo Cantarovich, Julio F. Fiore, Rita S. Suri, David Landsberg, Catherine Weber, Marie-Chantal Fortin

Bibliographic record

VenueTransplantation · 2022
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsUniversity of British ColumbiaUniversité de MontréalMcGill University Health Centre
Fundersnot available
KeywordsMedicineOddsCross-sectional studyLikert scaleOdds ratioFamily medicineLive donorReferralKidney transplantationTransplantationPsychologyLogistic regressionSurgeryInternal medicinePathology

Abstract

fetched live from OpenAlex

Background and objectives: Living donor kidney transplantation (LDKT) is the preferred therapeutic option for patients with end-stage kidney disease and health professionals (HPs) can provide unique insights to improving the delivery of LDKT to patients. We aimed to quantify system-level barriers to LDKT that we have previously identified in our qualitative work and estimate their association with LDKT performance. We also aimed to determine if HP’s characteristics influenced responses and what HPs thought should be priorities to increase LDKT. Methods: We conducted a cross-sectional survey of Canadian health professionals (HPs) and asked participants to rate statements on a Likert scale of 1-5 (strongly disagree-strongly agree). Statements captured themes related to communication, role perception, education/training/comfort, attitudes, referral process, patient as the barrier, and resources/infrastructure. The percentage of participants who agreed with these statements was analyzed and compared by LDKT performance (provincial living donation rates higher versus lower than the national average) and HP characteristics. Results: We obtained 353 complete responses. Overall, themes related to poor communication, poor role perception and HPs education/training/comfort emerged as barriers to LDKT. When compared with HPs from high-performing provinces, those from low-performing provinces had lower odds of agreeing that their province promoted LDKT (aOR=0.27, 95%CI:0.16-0.48). They also had lower odds of initiating discussions about LDKT (aOR=0.30, 95%CI:0.17-0.55), higher odds of agreeing that the transplant team is best suited to discuss LDKT (aOR=2.64, 95%CI:1.60-4.33) and that more resources would increase LDKT discussions from them (aOR=2.06, 95%CI:1.25-3.40). When comparing responses by HP characteristics, non-physician role and <10 years of experience were associated with the level of agreement across several themes. Creating guidelines, streamlining evaluations, and improving communication were ranked as priorities to increase LDKT over patient education. Conclusion: Poor communication between treating teams, poor role perception and HPs lack of education/training/comfort emerged as system-level barriers to LDKT and even HPs themselves identified addressing system-level inefficiencies as priorities to increase LDKT over patient education. More importantly, we report that poor role perception, low resources, and poor infrastructure may be driving differences in LDKT performance in a real-world setting. Our findings have policy implications and can build on the current patient-level work that others are pursuing to increase LDKT. This work is supported by a Research Innovation Grant from the Canadian Donation and Transplant Research Program and 2021 Canada Summer Jobs competition from the Employment and Social Development of the Government of Canada.

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)0.006
metaresearch head score (Gemma)0.017
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 score0.209
Threshold uncertainty score0.415

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.017
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.059
GPT teacher head0.359
Teacher spread0.300 · 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".

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Citations0
Published2022
Admission routes2
Has abstractyes

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