Bibliographic record
Abstract
BACKGROUND: Since the release of the Tri-Council Policy Statement (TCPS), there has been a growing interest in research ethics concommitant with an increase in the use of qualitative design for health research. Qualitative studies present unique ethical problems that may be poorly understood by researchers and research ethics boards (REBs). OBJECTIVE: To describe the ethical problems in qualitative research, and to make recommendations that will help researchers develop ethical qualitative proposals, and help REBs review these proposals. METHOD: Review of literature and philosophical analysis. RESULTS AND CONCLUSION: Qualitative studies raise unique issues with respect to methods, protection from harm, informed consent, privacy, and confidentiality, all of which are central to the principles of the TCPS. The problems are partly inherent, as the design tends to emerge as the study proceeds, and the research question may change. Researchers and REBs must become more aware, through education and critical reflection, of the types of problems that might arise in these studies, and of the approaches that might be taken to minimize risk to participants.
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 | 0.261 | 0.252 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".