Examining the Ethical Implications of Health Care Technology Described in US and Swedish PhD Dissertations: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: The development of new biomedical technologies is accelerating at an unprecedented speed. These new technologies will undoubtedly bring solutions to long-standing problems and health conditions. However, they will likely also have unintended effects or ethical implications accompanying them. It may be presumed that the research behind new technologies has been evaluated from an ethical perspective; however, the evidence that this has been done is scant. OBJECTIVE: This study aims to understand whether and in what manner PhD dissertations focused on health technologies describe actual or possible ethical issues resulting from their research. METHODS: The purpose of scoping reviews is to map a topic in the literature comprehensively and systematically to identify gaps in the literature or identify key evidence. The search strategy for this protocol will include electronic databases (eg, ProQuest, PubMed, Diva, SwePub, and LIBRIS). Searches will be limited to PhD dissertations published in the United States and Sweden in the last 10 years. The study will be mapped in 5 stages: (1) identifying the research question, (2) identifying relevant studies, (3) study selection, (4) retrieving and charting the data, and (5) collating, summarizing, and reporting the results. RESULTS: The findings of this study will indicate if and how researchers, PhD students, and their supervisors are considering ethics in their studies, including both research ethics and the ethical implications of their work. The findings can guide researchers in determining gaps and shortcomings in current doctoral education and offer a foundation to adjusting doctoral research education. CONCLUSIONS: In a society where technology and research are advancing at speeds unknown to us before, we need to find new and more efficient ways to consider ethical issues and address them in a timely manner. This study will offer an understanding of how ethics is currently being integrated into US and Swedish PhD dissertations and inform the future direction of ethics education at a doctoral level. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/14157.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
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 | 0.168 | 0.225 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.053 | 0.014 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".