Access to science for junior doctors and neurologists in French-speaking countries: challenges and future perspectives
Bibliographic record
Abstract
Abstract Background and objectives:Science education developed historically from experimentation science to model theories of cognition. Digitization in medical science brought about new challenges of access to science for education and publishing. The aims of our study are to describe the differences in access to science and scientific publications for junior doctors and neurologists in French-speaking countries, and to identify difficulties and their association with demographic, workplace, social and personal factors.Methods:We performed a thirty-nine-question-survey to define access to science from two major perspectives, scientific education, and scientific publishing. We explored scientific education through demographic data and scientific resources (institutional, online, personal), and evaluated scientific publishing of thesis and articles according to demographic data, number of publications, and difficulties with publishing.Results:Our study identified personal and environmental factors interfering with scientific access, some of which are attributed to junior doctors and neurologists in French-speaking countries as age, gender, ethnicity, income and work and life-balance. A heavier load was observed for African scientists. The main scientific resources used for medical education were Journals 82,9%, Congresses 79,4%, and Sci-Hub 74,5%. Junior scientists are facing major difficulties in writing in science due to linguistic (56,5%), financial (64,7%), scientific (55,3%), and logistic (65,3%) factors.Conclusions:This paper suggests that ethnicity, age, gender, and work-life balance can all impact access to science at different levels. The challenge now is to create digital platforms that modernize medical education and help build bridges for research within diverse scientific communities.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".