Effect of Demographic Factors on Openness to Medical Technology
Bibliographic record
Abstract
What are the factors that cause societies to readily accept some technologies and resist other technologies?The success or failure of a variety of technologies, including vaccines, gene editing, artificial intelligence, and brain-computer interfaces, depends on societal acceptance of these technologies.The objective of this research is to examine the relationship between technological acceptance and geography, gender, lifestyle, religion, and age.A review of the current literature reveals that gender may influence technological acceptance.For example, a study of adults in Germany, Poland, and Turkey found that women displayed much higher acceptance of medical technology than men.Lifestyle factors also play a role, as individuals who exercised more frequently were more open to technological innovations.Differences in economic development may influence acceptance of novel technologies, as those who live in less developed countries may be more open to technological advancements that will bring about economic advancement, whereas those who live in more developed societies may be more focused on the risks of new technologies.Finally, religious beliefs can play an important part in technological acceptance or resistance.In the United States, adults who expressed a high level of religious commitment were more likely to view new technologies such as gene editing as meddling with nature, while adults who expressed a low level of religious commitment were more likely to view new technologies positively.Education level additionally correlates with acceptance to technological innovation.Cultural factors may also account for the differences in technological acceptance; further research is required in this area.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".