Essentials of research ethics for healthcare professionals
Bibliographic record
Abstract
Whether conducted in a university or the healthcare field, research with human subjects gives rise to a multitude of ethical questions for healthcare professionals. While engaging in ongoing professional development on how to conduct research ethically, both clinicians and scientists need to expand their knowledge to provide answers to the following questions: Which ethical theories serve as a foundation for ethical principles in research ethics? What ethical principles should a researcher respect when conducting research with human subjects? What does it mean to conduct research ethically? What ethical dilemmas are encountered by a researcher in conducting research? This paper provides a review of ethical theories, and the ethical basis of guidelines developed and used to guide human subject research. Ethical behaviors and the personal responsibility of the researcher conducting research with human subjects are discussed along with the ethical considerations in research designs and methods.
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 | 0.230 | 0.269 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.013 | 0.020 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".