Doing the right thing! A model for building a successful hospital-based ethics committee in Nunavut
Bibliographic record
Abstract
BACKGROUND: There exists a need throughout the North to increase capacity to address issues of health ethics and for community members to better understand and share their perspectives on this topic. Ethics comes down to weighing rights and wrongs, evaluating differing needs and understandings, acknowledging the many shades of grey and doing our best to come up with the just, fair and moral approach to the question at hand. Northern regions must collaborate to share capacity, successes and experiences in order to meet the unique needs of northern health care institutions and move forward on this issue. While guidelines for ethical research with indigenous populations exist, little has been published about an Inuit approach to health ethics more broadly. DESIGN: To fill a critical need and to meet accreditation standards, the Qikiqtani General Hospital (QGH) in Iqaluit, Nunavut, Canada, is in the process of building an Ethics Committee. Capitalizing on partnerships with other bodies both in northern and southern Canada has proved an efficient and effective way to develop local solutions to challenges that have been experienced both at QGH and other jurisdictions. METHODS: The Ottawa Hospital Ethics Office and the active ethics committee at Stanton General Hospital in Yellowknife, NT, contributed expertise and experience, and helped provide some direction for the QGH ethics committee. At the local level, based on our shared commitment to health care ethics, the Qaujigiartiit Health Research Centre is an invaluable partner whose parallel efforts to develop a northern Health Research Ethics Board (REB) gives great synergy to the QGH Ethics Committee. RESULTS: Passion and commitment, as well as administrative support and endorsement from health care leaders, are the aspects of successful initiatives that we have identified to date. Using the information from both the experiences of other partners, as well as information gathered at a retreat held in Iqaluit in September 2011, we are working to develop a model for the QGH ethics committee that incorporates multi-level perspectives, from that of community to that of front-line worker. CONCLUSION: Ideally, the scope of the QGH Ethics Committee will grow over time to include ethics education, facilitation of clinical ethical consults, ethical review of policy, advice on governance issues and involvement and support of an external northern Health REB.
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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 | 0.095 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.054 | 0.041 |
| Scholarly communication | 0.028 | 0.015 |
| Open science | 0.009 | 0.031 |
| Research integrity | 0.010 | 0.013 |
| 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".