The acceptability of nanocarriers for drug delivery in different contexts of use: perceptions of researchers and research trainees in the field of new technologies
Bibliographic record
Abstract
BACKGROUND: Despite marked optimism in the field of nanomedicine about the use of drug-delivery nanocarriers, uncertainties exist concerning nanocarriers' possible unintended impacts and effects. These uncertainties could affect user acceptance and acceptability. "Acceptance" refers to the intention to put a technology or a device to a specified use. "Acceptability" refers to a value judgment that accounts for acceptance. The objectives of this study were to characterize impact perception, acceptance, and acceptability in relation to drug-delivery nanocarriers in different contexts of use, and to explore relationships among these concepts. METHODS: A sample of European and Canadian researchers and graduate research trainees active in the field of new technologies was recruited by targeted email invitation for participation in a web-based questionnaire study. The questionnaire presented scenarios for two contexts of use (lung cancer, seasonal flu) of drug-delivery nanocarriers with two compositions (carbon, synthetic DNA). Respondents' impact perception, acceptance, and acceptability judgment in relation to each kind of nanocarrier in each context of use were measured with Likert scale questions and scored using categorical values. RESULTS: Two hundred and fourteen researchers and graduate research trainees completed the questionnaire. The results showed that nanocarrier composition influenced impact perception: as compared with the carbon nanocarrier impact perception, the positive impacts of the synthetic DNA nanocarrier were perceived as more significant and more likely to occur than its negative impacts. Composition did not influence acceptance or acceptability. Context of use significantly influenced acceptance and acceptability of both kinds of nanocarriers: researchers were more likely to accept the use of nanocarriers to treat lung cancer than the seasonal flu. The results also showed a significant relationship between acceptance and the perceived usefulness of the treatments. CONCLUSION: Nanocarrier composition does not appear to influence acceptance or acceptability. On the other hand, the nanocarriers' perceived usefulness and context of use are both major factors in accounting for acceptance and acceptability.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".