Survey of Public Health Professionals on perspectives towards Direct-to-Consumer Genetic Tests
Bibliographic record
Abstract
Abstract Background The diffusion of Direct-to-Consumer Genetic Tests (DTC-GTs) requires properly literate professionals to appropriately address the challenges they pose to Public Health (PH). Hence, we conducted a survey to assess the current state of knowledge, attitudes, and behaviors of PH professionals members of the European Public Health Association (EUPHA) towards DTC-GTs. Methods The results of a scoping review conducted on PubMed to identify articles on healthcare professionals' perspectives towards DTC-GTs were used to elaborate a survey, which was validated through a focus group with experts. EUPHA members were invited to participate through email invitation, and EUPHA and EUPHAnxt newsletters. We performed a descriptive analysis of the results, and a univariate analysis to evaluate associations between selected variables. Results Two hundred and seventy-seven PH professionals (44.4% male, 55.6% female) completed the survey, 67.1% of whom were not involved in genetics or genomics within their professional activities. Although 78.0% were that companies are advertising and selling genetic tests directly to consumers, most of them reported a low level of awareness towards DTC-GTs applications and regulation aspects. The majority reported that a qualified health professional be involved in the DTC-GT process (91.7%), and did not feel prepared to address citizens' questions (64.6%). Eventually, 72.6% of the respondents stated that the provision of DTC-GT should be regulated on a national level, similarly to medicines. Conclusions Our results revealed a high level of awareness about the existence of DTC-GTs. Despite the overall positive attitudes, PH professionals reported a high need for strengthening regulatory aspects of DTC-GTs provision process. The growing interest of DTC-GTs among citizens and the poor knowledge of PH Professionals about these tests highlights the need to identify strategies for literacy improvement. Key messages Given the challenges for health systems, improving Public Health professionals' literacy on new technologies such as Direct-to-Consumer Genetic Tests is essential. The need for strengthening the regulation of Direct-to-Consumer Genetic Tests expressed Public Health professionals should guide policymakers.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".