Intégrer l’infosurveillance, l’infodémiologie et une recherche interventionnelle conséquente dans nos systèmes de santé publique pour mieux protéger et promouvoir la santé de la population au Canada : idées et perspectives
Bibliographic record
Abstract
There is no longer any doubt that exposure to the tsunami of health information which is sometimes evidence-based and sometimes unfounded and even misleading, is a public health issue. The term infodemic is used to describe this phenomenon. Research conducted over the past two decades has provided a measure of the extent of information overload and of the quality of information to which populations are exposed. Selected harmful effects have also been observed. It is urgent to mobilize and structure public health systems by involving all the required expertise to combat health misinformation and better manage the infodemic. Towards this end, we are launching a call for critical thinking around three themes: the infosphere as a social determinant of health, the development of skills in infodemiology, and finally, the development, cocreation, and evaluation of consequential interventions to better manage the infodemic and combat disinformation. We believe that lessons learned collectively from the successful integration of infoveillance, infodemiology, and consequential intervention research in our public health systems will serve to better address issues emerging from infodemics.
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.
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 | 0.044 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".