A New Method of Building Patients’ Health-Awareness in Uro-Oncology
Bibliographic record
Abstract
BackgroundDespite vast scientific evidence supporting health-awareness in the prevention and treatment outcome of cancer, studies comparing the effectiveness of different educational methods in in raising patients’ health-awareness are lacking.ObjectivesWe present and evaluate a new patients’ decision-making aid—an educational method based on staging mock medical appointments at a urological office.Materials and MethodsFour different “real-life scenarios” addressing prostate, kidney, bladder, and testicular cancers were prepared and played out by health professionals. The participants (n = 181) who observed the scenes were asked to fill in a questionnaire prepared by the authors. Results were then analysed statistically; P-value < 0.05 was considered significant.ResultsA statistically significant difference was found in assessing the intelligibility of the presented material depending on the participants’ level of education and where they lived (eg, village, town, city). According to 95% of the participants, the educational method provided during our meeting contributed to a significant increase in their knowledge of cancer. Moreover, 89% expressed their need for further education.ConclusionBuilding patients’ health-awareness by health professionals is important and may influence therapy outcome. The effectiveness and perception of our method by patients require further research and evaluation; however, the presented results seem promising.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".