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Record W4402816186 · doi:10.1177/20543581241276362

Characteristics and Practices of High-Performing Centers in Organ Donor Identification and Referral: A Qualitative Study.

2024· article· en· W4402816186 on OpenAlexaffabout
Leahora Rotteau, Samuel Vaillancourt, Mercedes Magaz, Lisha Lo, Brian M. Wong, Jehan Lalani, Sam D. Shemie, Samara Zavalkoff

Bibliographic record

VenuePubMed · 2024
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsMcGill UniversityMcGill University Health CentreSt. Michael's HospitalCanadian Blood ServicesUniversity of Toronto
Fundersnot available
KeywordsMedicineReferralQualitative researchIdentification (biology)Intensive care medicineFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

Background: The identification and referral (ID&R) of potential organ donors to provincial organ donation organizations (ODOs) is a critical first step in the organ donation process. However, even in provinces with mandatory referral legislation, there remains variability in ID&R rates across critical care units, with some units demonstrating high performance despite experiencing similar constraints associated with existing structures, policies, and practices. Objective: We sought to identify the enablers and specific strategies that high-performing critical care units leveraged to achieve their exceptional performance. Design: We conducted a descriptive qualitative study to inform ID&R improvement efforts as part of a positive deviance initiative. Setting: We identified three high-performing critical care units as study sites. Participants: Clinicians working in identified critical care units. Methods: At each site, we interviewed clinical team members about their perceptions and experiences of ID&R. Data analysis followed a thematic analysis approach. Results: We outline three themes describing how the high-performing hospitals achieve strong ID&R practices. First, all units demonstrated a high degree of integration between the concepts of high-quality end-of-life care and organ donation. Team members were consistently notified of successful transplants stemming from their unit, and all missed ID&Rs were tracked and discussed. Second, participants described a team approach with strong medical leadership, where all team members embrace their role in ensuring that no potential donor is missed. Finally, the units adopted strategies to support and simplify ID&R such as collectively simplifying triggers for referral, developing strong working relationships with provincial donor coordinators, and creating informal avenues of communication between clinicians and donor coordinators. Limitations: The lack of comparable data for potential organ donor referral rates across Canada impacted our ability to identify high-performing hospitals based on data. Instead, we contacted the ODOs directly to identify high-performing units that met our criteria. Second, our study sample was limited to three hospital sites from three different provinces and the three hospitals perform organ recovery and transplant on-site. Conclusion: Critical care units can adopt strategies and implement interventions to support ID&R improvement efforts. We provide examples informed by this study. We also highlight considerations that require attention when engaging in this work such as ensuring that all team members are aware of changes in care plans and physicians consistently engage in discussions about organ donation. Local medical leadership is critical to supporting these changes.

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)0.012
metaresearch head score (Gemma)0.019
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 score0.030
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.019
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0100.007
Scholarly communication0.0040.003
Open science0.0020.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.051
GPT teacher head0.351
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 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

Citations2
Published2024
Admission routes2
Has abstractyes

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