Implementing a Clinical Ethics Needs Assessment Survey: Results of a Pilot Study (Part 2 of 2)
Bibliographic record
Abstract
This paper details the implementation of the Clinical Ethics Needs Assessment Survey (CENAS) through a pilot study in five units within Hamilton Health Sciences. We describe how these pilot sites were selected, how we implemented the survey, the significant results and our interpretation of the findings. The primary goal of this paper is to share our experiences using this tool, specifically the challenges we encountered conducting a staff ethics needs assessment across different units in a large teaching hospital, and the facilitators to our success. We conclude with a discussion of the limitations of this study, our plans for using the results to develop a proactive ethics education strategy, and suggestions for other organizations wishing to adapt the CENAS to assess their staff ethics needs. Our secondary goal is to advance the "quality agenda" for ethics programs by demonstrating how a tool like the CENAS can be used to design more effective educational interventions, and to support strategic planning and proactive priority-setting for ethics programs.
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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 | 0.052 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".