A Conceptual Framework for Evaluating National Organ Donation and Transplantation Programs
Bibliographic record
Abstract
Conceptual frameworks are valuable resources that can be used to guide the planning, evaluation, and development of healthcare services. However, there are currently no comprehensive frameworks focused on organ donation and transplantation that identify the critical factors underlying a successful national program. To address this knowledge gap, we developed a conceptual framework that takes into account all major domains of influence, including political and societal aspects as well as clinical implementation. The framework was initially constructed based on a targeted review of the relevant medical literature. Feedback provided by a panel of international experts was incorporated into the framework via an iterative process. The final framework features 16 essential domains that are critical for initiating and maintaining a successful program and improving the health of patients with organ failure. Of particular note, these domains are subject to three overarching health system principles: responsiveness, efficiency, and equity. This framework represents a first attempt to develop a whole-system view of the various factors that contribute to the success of a national program. These findings provide a useful tool that can be adapted to any jurisdiction and used to plan, evaluate, and improve organ donation and transplantation programs.
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 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 | 0.157 | 0.101 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.026 | 0.016 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.022 | 0.019 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".