Attitudes Regarding Science Knowledge and Clinical Trials
Bibliographic record
Abstract
Clinical trials are vital to advancements in management of cancer and improved patient outcomes. However, few cancer patients are enrolled in trials in Ontario. One contributing factor to this discrepancy is a lack of general awareness in the community about the scientific process and the importance of clinical trials. This project aims to assess attitudes in Windsor-Essex County regarding scientific awareness and participation in clinical trials. This survey is targeted towards past or present cancer patients, family members, and/or caregivers in Windsor-Essex. It asks questions about participants' demographics, their perceived understanding of science and clinical research, and their attitudes regarding clinical trial enrolment. It will also identify whether participants have participated in clinical trials. At the end of the survey, participants will be asked if they would be interested in participating in future studies that will utilize individual interview methodologies and focus groups methodologies to obtain more qualitative information about identified lived experiences and perspectives. This information will then be used in conjunction with future studies to develop effective educational programs about clinical trials and the scientific research process. These programs can then be used in the future to increase scientific knowledge translation, in the hopes of helping patients to make informed decisions about clinical trial enrollment. It is anticipated that educating the patient community about the fundamentals of scientific research will improve their understanding of the importance of clinical trials and positively impact their attitudes towards participation in clinical trials.
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 | 0.070 | 0.139 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".