Evaluating Marketing Strategies in Organ Donation and Transplantation
Bibliographic record
Abstract
Patients suffering from end-stage diseases wait in expectation of life-saving organs that could improve their quality of life. However, there is widening gap between organ supply an demand. The intention of this study is to explore and evaluate marketing strategies in organ donation and transplantation. In an attempt to achieve the purpose of this study, a qualitative approach was employed. Phenomenology was used as the study’s research design. The study used social marketing and the theory of social constructivism as the theoretical frameworks and data was collected through in-depths interviews. Qualitative data was analysed through thematic content analysis. Purposive sampling was used to select 30 organ donation coordinators. The study established that public education is the main vehicle through which organ donation and transplantation are promoted. Educational talks, distribution of information, media, social media, expos, awareness events, and corporate and educational talks are amongst the strategies used to promote organ donation. The study recommends that the Department of Education include the issue organ donation in school curricula, and that religious organisations, regular worksite campaigns, regular television advertisements should be used as vehicles through which to promote organ donation and transplantation. Furthermore, it is recommended that additional public awareness campaigns be held in black communities. It is respectfully recommended that the Organ Donor Foundation consider opening satellite offices in all nine South African provinces.
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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".