DORA-compliant measures of research quality and impact to assess the performance of researchers in biomedical institutions: Review of published research, international best practice and Delphi survey
Bibliographic record
Abstract
OBJECTIVE: The San Francisco Declaration on Research Assessment (DORA) advocates for assessing biomedical research quality and impact, yet academic organizations continue to employ traditional measures such as Journal Impact Factor. We aimed to identify and prioritize measures for assessing research quality and impact. METHODS: We conducted a review of published and grey literature to identify measures of research quality and impact, which we included in an online survey. We assembled a panel of researchers and research leaders, and conducted a two-round Delphi survey to prioritize measures rated as high (rated 6 or 7 by ≥ 80% of respondents) or moderate (rated 6 or 7 by ≥ 50% of respondents) importance. RESULTS: We identified 50 measures organized in 8 domains: relevance of the research program, challenges to research program, or productivity, team/open science, funding, innovations, publications, other dissemination, and impact. Rating of measures by 44 panelists (60%) in Round One and 24 (55%) in Round Two of a Delphi survey resulted in consensus on the high importance of 5 measures: research advances existing knowledge, research plan is innovative, an independent body of research (or fundamental role) supported by peer-reviewed research funding, research outputs relevant to discipline, and quality of the content of publications. Five measures achieved consensus on moderate importance: challenges to research productivity, potential to improve health or healthcare, team science, collaboration, and recognition by professional societies or academic bodies. There was high congruence between researchers and research leaders across disciplines. CONCLUSIONS: Our work contributes to the field by identifying 10 DORA-compliant measures of research quality and impact, a more comprehensive and explicit set of measures than prior efforts. Research is needed to identify strategies to overcome barriers of use of DORA-compliant measures, and to "de-implement" traditional measures that do not uphold DORA principles yet are still in use.
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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: Evaluation · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Metaresearch Domain: Evaluation · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.619 | 0.552 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.046 | 0.030 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".