Characteristics and Impact of Librarian Co-authored Systematic Reviews: A Bibliometric Analysis
Bibliographic record
Abstract
ABSTRACT Background Health sciences libraries have been providing services that support systematic reviews (SRs) for many years. In recent times the problem facing health sciences libraries is the management of the demand versus resources availability. There have been questions posed as to the value of this type of service in health sciences libraries. A valuable outcome of librarian collaboration on SR teams is co-authorship of the reported SRs. This study aimed to examine the characteristics and impact of librarian co-authored SRs. Methods A bibliometric analysis was conducted. Librarian co-authored SRs were identified in the Web of Science (WOS) Core Collection limited up to the year 2017. Librarian co-authored SRs with the librarian as first author were excluded from this analysis. Additional inclusion and exclusion criteria were applied in the selection process. The included records were analyzed using Perl programs and VOSviewer. To examine the dissemination of librarian co-authored SRs, citing articles to the included SR records were retrieved from the WOS Core Collection and then identified in MEDLINE for an analysis of the indexed publication types. Results The included results yielded 1,711 librarian co-authored SRs published between 1996 and 2017. The top three countries of the first author affiliation were USA, Canada, and Netherlands. Sources of publication were distributed among 730 journal titles. The number of MEDLINE citing articles to the included SRs was 28,868. The mean number of citations to a SR was 26.4. The top publication type descriptor of the citing articles representing the MEDLINE “Study Characteristic” category was “Randomized Controlled Trial”. Conclusion Outcomes of librarian contributions to supporting SRs include increasing scholarship opportunities that highlight librarian contributions to other disciplines. SRs are bodies of evidence, which can influence policy, patient care, and future research. In this study, we demonstrate that librarian co-authored SRs are disseminated into randomized controlled trials and other study types, meta-analyses, as well as guidelines, thus providing insight into knowledge transfer and the potential for clinical implementation.
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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 | BibliometricsMetaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.149 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.025 | 0.011 |
| Bibliometrics | 0.087 | 0.207 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".