Understanding the Influence of Inter-Professional Relational Networks within Organ Donation Programs in Ontario
Bibliographic record
Abstract
Background To optimize organ donation performance the nature and impact of complex factors need to be understood. Some factors are well known and might not be modifiable (contra-indications for transplant, age limit of organ donor, etc), but others (collaboration and relationships) that contribute to variations among similar hospitals in the same province, are poorly understood. Thus, the overall aim of this research is to understand the nature and impact of relationships within the organizational context of organ donation programs in Ontario. Methods We will employ an exploratory, prospective, diagnostic study consisting of three approaches: (1) to describe the characteristics of social networks of health care professionals of the organ donation programs (Social Network Analysis) and (2) to describe the organizational attributes and processes of on organ donation programs (Multiple Case-Study), and (3) to compare the influence of social networks and organizational attributes on the performance of organ donation programs (Network Comparison). The study sites will include Ontario hospitals designated as type A based on trauma centre level (Public Hospitals Act classification); and hospitals partners of the Ontario’s Organ Procurement Organization. At least five sites will be purposely selected to capture the greatest variability of settings possible. Expected/Preliminary Outcomes Understanding the influences of informal networks on organ donation outcomes is essential to the development and informing of interventions to optimize performance. This research will increase organ donation rates in Ontario by providing to the scientific community a novel measure of organ donation programs’ performance and by identifying successful collaboration paths during organ donation processes. We will present the development process of the research as well as preliminary findings.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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".