Reference management software for systematic reviews and meta-analyses: an exploration of usage and usability
Bibliographic record
Abstract
BACKGROUND: Reference management software programs enable researchers to more easily organize and manage large volumes of references typically identified during the production of systematic reviews. The purpose of this study was to determine the extent to which authors are using reference management software to produce systematic reviews; identify which programs are used most frequently and rate their ease of use; and assess the degree to which software usage is documented in published studies. METHODS: We reviewed the full text of systematic reviews published in core clinical journals indexed in ACP Journal Club from 2008 to November 2011 to determine the extent to which reference management software usage is reported in published reviews. We surveyed corresponding authors to verify and supplement information in published reports, and gather frequency and ease-of-use data on individual reference management programs. RESULTS: Of the 78 researchers who responded to our survey, 79.5% reported that they had used a reference management software package to prepare their review. Of these, 4.8% reported this usage in their published studies. EndNote, Reference Manager, and RefWorks were the programs of choice for more than 98% of authors who used this software. Comments with respect to ease-of-use issues focused on the integration of this software with other programs and computer interfaces, and the sharing of reference databases among researchers. CONCLUSIONS: Despite underreporting of use, reference management software is frequently adopted by authors of systematic reviews. The transparency, reproducibility and quality of systematic reviews may be enhanced through increased reporting of reference management software usage.
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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 | 0.670 | 0.897 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.013 |
| Bibliometrics | 0.049 | 0.057 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".