A Content Analysis of Systematic Review Online Library Guides
Bibliographic record
Abstract
Objective – Online library guides can serve as resources for students and researchers conducting systematic literature reviews. There is a need to develop learner-centered library guides to build capacity for systematic review skills. The objective of this study was to explore the content of existing systematic review library guides at research universities. Methods – We conducted a content analysis of systematic review library guides from English-speaking universities. We identified 18 institutions for inclusion using a Scopus search to find the institutions with the highest number of systematic review publications. We conducted a content analysis of those institutions’ library guides, coding for the types of resources included, and the stage of the systematic review process to which they referred. A chi-square test was used to determine whether the differences in distribution of the resource types within each systematic review stage were statistically significant. Results – The most common type of resource was informational in content. Only 24% of the content analysed was educational. The most common stage of the systematic review process was conducting searches. The chi-square test revealed significant differences for seven of the nine systematic review stages. Conclusion – We found that many library guides were heavily informational and lacking in instructional and skills focused content. There is a significant opportunity for librarians to turn their systematic review guides into practical learning tools through the development and assessment of online instructional tools to support student and researcher learning.
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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: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | 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 | 0.102 | 0.322 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.048 | 0.041 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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, 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".