Future healthcare logistics: a survey of the public opinion on drones in Denmark
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Drones are expected to become widespread in society, making public readiness an important prerequisite for successfully unleashing them. This article investigates Danish citizens’ opinions on drones across varying fields of application and, specifically, six potential cases of healthcare logistics. Survey data representative of age, gender, and geography were collected and included information about respondents’ background, knowledge level of drones, and opinions on different drone use cases. Data were analysed with frequency tables and bivariate cross-tabulation. A thousand and four Danish adults completed the survey. Although other fields of application received higher levels of support, a majority of the respondents were positive towards using drones for healthcare logistics. Transportation of medicine and blood samples between hospitals were the most accepted healthcare use cases. Support varies across age with the highest support found in the eldest age group. Also, the more citizens report to know about drones, the more they tend to support using them. The results suggest that policymakers and firms must be attentive towards the public opinion on drones and seek insights into what citizens regard as noble purposes of using drones. Moreover, citizens must become more acquainted with drones, as this will likely boost public support.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it