Deploying an ethics needs assessment to inform a navigational tool for research compliance pathways at a provincial Canadian health authority
Bibliographic record
Abstract
Practitioners aim to improve healthcare systems and clinical care through a variety of activities as part of a learning healthcare system. Yet the distinction between projects requiring Research Ethics Board (REB) approval or not is becoming increasingly blurred, making it difficult for researchers and others to classify projects and then navigate the required compliance pathway appropriately. To address this challenge, the Provincial Health Services Authority (PHSA) of British Columbia (BC) created a decision tool called the "PHSA Project Sorter Tool" to serve its diverse community while also meeting the unique needs of the BC regulatory and policy environment. The goal of the tool was to standardize and clarify organizational project review and ensure project leads were referred to the appropriate review body or service provider within the PHSA in the most efficient manner possible. In this paper, we describe the ethics needs assessment that was conducted to inform the tool and the results of our ongoing evaluation of the tool since it was launched in January, 2020. Our project shows that this simple tool can reduce burdens on staff and provide clarity to users by standardizing processes and terms and directing users to appropriate internal resources.
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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 | 0.097 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.004 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".