Advancing discussion of ethics in mixed methods health services research
Bibliographic record
Abstract
BACKGROUND: To describe the ethical issues and experiences of scientists conducting mixed methods health services research and to advance empirical and conceptual discussion on ethical integrity in mixed methods health research. METHODS: The study was conducted with 64 scholars, faculty and consultants from the NIH-funded Mixed Methods Research Training Program (MMRTP) for the Health Sciences. This was a cross-sectional study. Survey results were analyzed using descriptive statistics to characterize responses and open coding to summarize strategies about eight ethical mixed methods research issues. Respondents completed an online survey to elicit experiences related to eight ethical issues (informed consent, confidentiality, data management, burden, safety, equitable recruitment, communication, and dissemination) and strategies for addressing them. RESULTS: Only about one-third of respondents thought their research ethics training helped them plan, conduct, or report mixed methods research. The most frequently occurring ethical issues were participant burden, dissemination and equitable recruitment (> 70% endorsement). Despite occurring frequently, < 50% of respondents rated each ethical issue as challenging. The most challenging ethical issues were related to managing participant burden, communication, and dissemination. Strategies reported to address ethical issues were largely not specific or unique to mixed methods with the exception of strategies to mitigate participant burden and, to a lesser degree, to facilitate equitable recruitment and promote dissemination of project results. CONCLUSIONS: Mixed methods health researchers reported encountering ethical issues often yet varying levels of difficulty and effectiveness in the strategies used to mitigate ethical issues. This study highlights some of the unique challenges faced by mixed methods researchers to plan for and appropriately respond to arising ethical issues such as managing participant burden and confidentiality across data sources and utilizing effective communication and dissemination strategies particularly when working with a multidisciplinary research team. As one of the first empirical studies to examine mixed methods research ethics, our findings highlight the need for greater attention to ethics in health services mixed methods research and training.
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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 | 0.633 | 0.609 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.026 | 0.110 |
| Scholarly communication | 0.043 | 0.040 |
| Open science | 0.009 | 0.043 |
| Research integrity | 0.024 | 0.037 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".