Current landscape of research ethics consultation services: National survey results
Bibliographic record
Abstract
Introduction: The goal of a research ethics consultation service (RECS) is to assist relevant parties in navigating the ethical issues they encounter in conduct of research. The goal of this survey was to describe the current landscape of research ethics consultation and document if and how it has changed over the last decade. Methods: The survey instrument was based on the survey previously circulated. We included a number of survey domains from the previous survey with the goal of direct comparison of outcomes. The survey was sent to 57 RECS in the USA and Canada. Results: Forty-nine surveys were completed for an overall response rate of 86%. With the passing of 10 years, the volume of consults received by RECS surveyed has increased. The number of consults received by a subset of RECS remains low. RECS continues to receive requests for consults from a wide range of stakeholders. About a quarter of RECS surveyed actively evaluate their services, primarily through satisfaction surveys routinely shared with requestors. The number of RECS evaluating their services has increased. We identified a group of eight key competencies respondents find as key to providing RECS. Conclusions: The findings from our survey demonstrate that there have been growth and development of RECS since 2010. Further developing evaluation and competency guidelines will help existing RECS continue to grow and facilitate newly established RECS maturation. Both will allow RECS personnel to better serve their institutions and add value to the research conducted.
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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 | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".