Analyses of Acceptability Judgments Made Toward the Use of Nanocarrier-Based Targeted Drug Delivery: Interviews with Researchers and Research Trainees in the Field of New Technologies
Bibliographic record
Abstract
The assessment of nanotechnology applications such as nanocarrier-based targeted drug delivery (TDD) has historically been based mostly on toxicological and safety aspects. The use of nanocarriers for TDD, a leading-edge nanomedical application, has received little study from the angle of experts’ perceptions and acceptability, which may be reflected in how TDD applications are developed. In recent years, numerous authors have maintained that TDD assessment should also take into account impacts on ethical, environmental, economic, legal, and social (E 3 LS) issues in order to lead to socially responsible innovation. Semi-structured interviews ( n = 22) were conducted with French and Canadian researchers and research trainees with diverse disciplinary backgrounds and involved in research related to emerging technologies. The interviews focussed on scenarios presenting two types of TDD nanocarriers (carbon, synthetic DNA) in two contexts of use (lung cancer, seasonal flu). Content and inductive analyses of interviews showed how facets of perceived impacts such as health, environment, social cohabitation, economy, life and death, representations of the human being and nature, and technoscience were weighed in acceptability judgments. The analyses also revealed that contextual factors related to device (nature of the treatment), to use (gravity of the disease), and to user (culture) influenced the weighting assigned to perceived impacts and thus contributed to variability in interviewees’ judgments of acceptability. Giving consideration to researchers’ perspective could accompany first steps of implementation and development of nanomedicine by producing a first, but wide, picture of the acceptability of nanocarrier-based TDD.
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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 | 0.050 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".