Incorporating biobanking into the future of healthcare: exploring patient and healthcare worker perspectives at a Canadian tertiary academic hospital
Bibliographic record
Abstract
Biobanks are an essential resource for researchers conducting scientific and translational research but require significant support from institutions and healthcare workers (HW) to operate and are reliant on patient consent and participation. In order to better understand the barriers to institution-wide biobanking, we conducted a survey to examine the knowledge, attitudes and concerns of patients and HW on a range of biobanking-related topics, including consenting practices, privacy and trust in the healthcare team and researchers, and current practices at Sunnybrook Health Sciences Centre. Overall, we found that there is strong patient and HW support for biobanking as a resource for research (89-96%). Furthermore, the majority 53% of HW are willing to incorporate biobanking into their clinical workflow and 39% had a neutral response. Encouragingly, patients possess a high level of trust in their healthcare team (80-99%). The main concerns regarding sample donation were 'breaches of privacy' and 'genetic information being used in an exclusionary (discriminatory) fashion.' Concerns around specimen utilization emerged as a major theme from HW. These results will inform and enhance future biobanking practices to improve the patient experience and increase patient engagement while streamlining specimen collection and utilization for scientific research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".