Impact of a course in evidence-informed policy-making on the acquisition of methodological knowledge: Findings from before-and-after studies conducted on three consecutive cohorts of master students
Bibliographic record
Abstract
The practice of evidence-informed policy-making (EIPM) consists of systematically searching, analyzing, synthesizing and disseminating the best available research evidence to inform decision-makers about policy problems, policy tools, implementation options, and/or policy evaluation results. Identifying the best available scientific evidence is not a simple task. The vast majority of research evidence contains risks of bias that hinder the reliability of their conclusions. In order to select the soundest available research evidence, policy analysts need to know how to critically appraise research evidence and identify different risks of bias. Formal theories on expertise acquisition in public bureaucracies suggest that these skills and knowledge should be acquired within academia rather than within governmental agencies. We thus created a 45-hour course in EIPM, POL-7061, that was first offered in 2012 to students enrolled in the Master’s Program in Public Affairs at Université Laval (Québec, Canada). The course mainly teaches techniques for searching and appraising different types of empirical studies. In 2013, we conducted a before-and-after study to assess the impact of the course on the methodological knowledge of the students. We repeated the exercise on two consecutive cohorts in 2014 and 2015. Mean percent of pre-post improvement on the knowledge test was 37% for the 2013 cohort, 51% for the 2014 cohort and 31% for the cohort of 2015. Teaching techniques in EIPM to Master’s students in public affairs is thus feasible and can have a positive impact on their basic methodological knowledge.
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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 | 0.037 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".