Challenges facing early-career and mid-career researchers: potential solutions to safeguard the future of evidence-based medicine
Bibliographic record
Abstract
The challenges facing the evidence-based medicine (EBM) movement are well documented.1 2 Yet, the problems facing early-career and mid-career researchers (EMCRs) working in the ecosystem of EBM have not been articulated. The coming together of a cohort of EMCRs from across the globe enabled this articulation.3 The 2019 EBMLive conference (see box 1) provided a space for EMCRs to discuss problems, exchange ideas and create a list of potential solutions. This article outlines four key problems faced by EMCRs and their potential solutions (see box 2). Box 1 ### The EBMLive Conference The EBMLive Conference (www.ebmlive.org) is a joint partnership between the Centre for Evidence-Based Medicine and the BMJ , designed to develop, disseminate, and implement better evidence for better healthcare . Since inception, EBMLive has worked tirelessly to include the voice of students and early-career researchers. Building on previous work, the inaugural Doug Altman Scholarship3 and Building Capacity Bursaries were launched in 2019 to fund the travel and attendance of early-career researchers from across the globe to attend the conference. Box 2 ### Problems facing EMCR and their potential solutions In the lead up to the EBMLive conference, Doug Altman Scholars submitted personal and general problems they have faced as early-career researchers. The responses were synthesised and shared with the Scholars to generate further discussion. During EBMLive, the problems and ideas for potential solutions were discussed and presented during dedicated sessions for early- and mid-career researchers. The key list of problems facing early-career and mid-career researchers and their potential solutions are as follows: 1. Tokenistic training of evidence-based medicine 2. Emphasis of quantity over quality 3. Lack …
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Incentives · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Metaresearch Domain: Incentives · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | medium |
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 | 0.022 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".