A Framework for Negotiating Ethics in Sensitive Settings: Hospice as a Case Study: Table 1.
Bibliographic record
Abstract
In this article, we explore our role and ethical obligations as human–computer interaction (HCI) researchers who operate in, and design for, sensitive settings. Recognizing a lack of clear ethical direction from any one code of ethics (COE), we analyze across COEs offered by the professional associations related to our team's research backgrounds to develop a framework for exploring ethical dilemmas in HCI research. While the individual COE tended to be overly specific and prescriptive, our framework highlights common concerns, applicable to a broad range of contexts. We then apply this framework to reflect on two ethical dilemmas, we faced during our work with hospice patients and their families. Through this exercise, we demonstrate how the framework can be applied to ethical dilemmas in HCI research. Draws attention to the ethical subtleties of HCI research in sensitive settings Reflects on the role of professional codes of ethics in research design and practice Analyzes professional codes of ethics to create a framework for ethical reflection Illustrates how the framework can be applied to ethical dilemmas in HCI research
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.022 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".