Systematic review support received and needed by researchers: a survey of libraries supporting Ontario medical schools
Bibliographic record
Abstract
Introduction: Finding efficient ways to meet the growing demand for library systematic review support is imperative for facilitating the production of high-quality research. The objectives of this study were threefold: 1) to ascertain the systematic review support provided by health sciences libraries at Ontario medical schools and their affiliated hospitals, 2) to determine the perceived educational needs by researchers at these institutions, and 3) to assess the potential usefulness of freely available, online educational modules for researchers that discuss all stages of the systematic review process. Methods: We conducted a cross-sectional survey in June and July of 2020. Data was analyzed and presented using median and interquartile range (IQR) for continuous measures, and in proportions for categorical measures. Results: 13 of 19 libraries invited provided usable data. Most libraries spent more time supporting systematic reviews via collaboration and participation than by providing educational support. The perceived needs of library users were contrary to the perceived gaps in researcher support provided by the library/institution. All libraries reported they would find freely available, online educational modules useful for training researchers. Discussion: The next steps for our inter-professional research team will be to develop freely available, online education modules that introduce researchers to all stages of the systematic review process. These modules cannot replace the value that direct support from librarians, biostatisticians or methodology experts can provide, however, they may offer a more efficient way for libraries to familiarize researchers and trainees with best practices and universally accepted reporting guidelines for performing a high-quality review.
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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 | 0.077 | 0.293 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.024 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".