Evaluating the Implementation of Ontario’s Organ and Tissue Donation Physician Leadership Model: Mapping a Way Forward
Bibliographic record
Abstract
Aimee Sarti,1 Stephanie Sutherland,1 Angele Landriault,2 Sonny Dhanani,3 Andrew Healey,4 Pierre Cardinal1 1Department of Critical Care, Ottawa Hospital, Ottawa, ON, Canada; 2Practice and Performance Unit, Royal College of Physicians and Surgeons of Canada (RCPSC), Ottawa, ON, Canada; 3Department of Pediatrics, University of Ottawa, Children’s Hospital of Eastern Ontario (CHEO), Ottawa, ON, Canada; 4Division of Emergency Medicine, Department of Medicine, McMaster University, Hamilton, ON, CanadaCorrespondence: Aimee Sarti Email asarti@toh.caBackground: The demand for solid organ transplantation has spurred countries around the world to search for innovative policies and practices to increase the supply of organs. Spain has become a global reference point for organ donation with the highest transplantation rates. In Ontario, Canada the Ontario Trillium Gift of Life (TGLN) has sought to replicate some of the successes in Spain. In particular, TGLN’s implementation of the Physician Leadership Model has been viewed as a promising strategy to improve donation conversion rates.Objective: The objective of this study was to evaluate the implementation of TGLNs (TGLN) Physician Leadership Model by examining critical implementation process variables (education/training, communication, satisfaction, participation and reach).Methods: This mixed-method implementation evaluation included data from all members of the Physician Leadership Model including the Chief Medical Officer, five Regional Medical Leads (RMLs), and the 52 Hospital Donation Physicians (HDPs). Social Network Analysis (SNA) surveys were sent to all 52 HDPs and yielded an 85% rate. Analysis included constructing sociograms and qualitatively analyzing interviews.Results: TGLN’s PLM was poised for success by utilizing the existing RMLs’ network as a foundation. The social network analysis measures, particularly participation and reach, indicated the PLM was quite dense (ie, the degree to which members are connected) at baseline. HDPs reported communication to be facilitated by their connections to their RMLs. Early evaluative data indicated that lack of education and training was viewed by HDPs as a barrier, and thus more capacity would need to be directed to this issue. Overall, HDPs reported that various intended outcomes were being met.Conclusion: We have demonstrated that an implementation evaluation helps us to understand which elements of the PLM were successful and which elements required immediate attention. This evaluation helped to highlight the successes and challenges in implementing the TGLN Physician Leadership Model in Ontario. Social network analysis of publicly funded capacity building systems has been identified as a promising area for health program evaluation to answer questions at a system level, such as identifying service provisions among information exchange networks and ultimately better health care.Keywords: implementation evaluation, organ and tissue donation, social network mapping, leadership
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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 | 0.028 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".