Barriers and Enablers to Organ Donation After Circulatory Determination of Death: A Qualitative Study Exploring the Beliefs of Frontline Intensive Care Unit Professionals and Organ Donor Coordinators
Bibliographic record
Abstract
BACKGROUND: A shortage of transplantable organs is a global problem. The purpose of this study was to explore frontline intensive care unit professionals' and organ donor coordinators' perceptions and beliefs around the process of, and the barriers and enablers to, donation after circulatory determination death (DCDD). METHODS: This qualitative descriptive study used a semistructured interview guide informed by the Theoretical Domains Framework to interview 55 key informants (physicians, nurses, and organ donation coordinators) in intensive care units (hospitals) and organ donation organizations across Canada. RESULTS: Interviews were analyzed using a 6-step systematic approach: coding, generation of specific beliefs, identification of themes, aggregation of themes into categories, assignment of barrier or enabler and analysis for shared and unique discipline barriers and enablers. Seven broad categories encompassing 29 themes of barriers (n = 21) and enablers (n = 4) to DCDD use were identified; n = 4 (14%) themes were conflicting, acting as barriers and enablers. Most themes (n = 26) were shared across the 3 key informant groups while n = 3 themes were unique to physicians. The top 3 shared barriers were: (1) DCDD education is needed for healthcare professionals, (2) a standardized and systematic screening process to identify potential DCDD donors is needed, and (3) practice variation across regions with respect to communication about DCDD with families. A limited number of differences were found by region. CONCLUSIONS: Multiple barriers and enablers to DCDD use were identified. These beliefs identify potential individual, team, organization, and system targets for behavior change interventions to increase DCDD rates which, in turn, should lead to more transplantation, reducing patient morbidity and mortality at a population level.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".