Understanding processes, structures and inter-professional relational networks within organ donation programs in Ontario
Bibliographic record
Abstract
Background: The complex factors contributing to variation in the performance of organ donation programs are poorly understood. Thus, the overall aim of this dissertation research was to enhance our understanding of the organizational attributes (i.e., process and structure) and inter-professional relationships within organ donation programs in Ontario. Objectives: The specific research objectives were: (1) To identify and synthesize the current evidence in regards to the key contexts, processes and structures of international organ donation programs, (2) To describe the interactions of the Organ Tissue Donor Coordinators (OTDCs) with others during organ donation cases, and (3) To describe the interprofessional interactions during organ donation cases, within organ donation programs in Ontario, from an organizational perspective (structure, context, process). Methods: To address Objective 1, I conducted a scoping review of the literature. To address Objective 2, I completed a social network analysis (SNA) of organ donation cases focusing on the OTDC role and interactions, and to address Objective 3, I completed a mixed method SNA with a convergent design. Data were collected among 3 Ontario hospitals through interviews, review of documents, and case observation. Results: For Objective 1, I identified three themes that influence success in organ donation programs: context (n=39, 46 %), process (n=48, 57 %), and structural (n=59, 70%). In the social network analysis (Objective 2), OTDCs were identified as the acting hub facilitating the information exchange in the network. For Objective 3, I identified that the care team regarded the OTDCs’ position as central in the network (contrary to the network analysis measures); and there were opportunities for improvement to reduce organ allocation time. Conclusions: This thesis’ findings reinforced positive aspects, previously presented in the literature, of the benefits of the OTDC role in managing organ donation processes. Based on our findings, we identified potential organ donation process improvements that could positively impact donation rates and reduce transplant waiting lists. However, in the future, it will be important to consider a national perspective that would allow comparisons across provinces to further explore the interprofessional roles and organizational attributes that are associated with optimal performance of organ donation networks.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".