A pilot study investigating the relationship between journal impact factor and methodological quality of real-world observational studies
Bibliographic record
Abstract
Introduction The primary objective of this study was to investigate the association between journal Impact Factor (IF) and study quality in real-world observational studies. The secondary objective was to explore whether the association changes as a function of different study factors (study design, funding type and geographic location). Methods Study quality was assessed using the Newcastle-Ottawa Scale (NOS). IFs were obtained from journal websites. The association between journal IF and NOS score was evaluated firstly using Spearman's correlation coefficient, and secondly using one-way Analysis of Variance (ANOVA). Results We selected 457 studies published in 208 journals across 11 consecutive systematic literature reviews (SLRs) conducted at our organization over the last 5 years. Most studies were cross-sectional and from North America or Europe. Mean (SD) NOS score was 6.6 (1.03) and mean ( SD ) IF was 5.2 (4.5). Overall, there was a weak positive correlation between NOS score and IF (Spearman's coefficient (ρ) = 0.23 [95% CI: 0.13–0.31]; p < 0.001). There was no correlation between NOS score and IF for prospective cohort studies (ρ = 0.07 [95% CI:−0.12–0.25]) and industry-funded studies (ρ = 0.06 [95% CI:−0.09–0.21]). Based on ANOVA, the effect size, eta squared (η 2 ), was 0.04 (95% CI: 0.01–0.08), indicating a small effect. Discussion While there is some correlation between journal quality and study quality, our findings indicate that high-quality research can be found in journals with lower IF, and assessing study quality requires careful review of study design, methodology, analysis, interpretation, and significance of the findings. Notably, in industry-funded studies, no correlation was found between methodological quality and IF.
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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 | 0.167 | 0.364 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".