Health Sciences Librarians’ Instructional Engagement in Continuing Education: A Scoping Review
Bibliographic record
Abstract
Objective: Healthcare professionals (HCPs) have an ongoing need for continuing education (CE) while Health Science Librarians (HSLs), accustomed to supporting a range of learning needs in a variety of contexts, are well situated to provide CE that addresses information retrieval, literacy, management, and more. To better understand the extent of HSL delivered CE activities, we undertook a scoping review to determine how HSLs instruct practicing HCPs in support of their CE. Methods: We searched for published and unpublished literature sources including PubMed (NCBI), Embase (Elsevier); Dissertations and Theses Global (ProQuest); CINAHL (EBSCO); Library, Information Science and Technology Abstracts (EBSCO); and Library Literature and Information Science Full Text (EBSCO). To identify unpublished sources, we searched the internet using Google and contacted two health sciences library listservs. We also performed backwards and forwards searching of our included sources. Results: Our database searches yielded 4842 sources, and we retrieved an additional 579 sources through supplementary retrieval methods. After duplicate removal and screening, we included 105 sources in this review. The included sources were published between 1970 to 2021 and covered a range of topics such as searching methods and tools, critical appraisal, and many more. Those related to evidence-based practice (EBP) appeared around 2001 and bibliometrics and bioinformatics arose after 2016. Publications depicting HSLs teaching CE most commonly occurred in academic settings. The most common population taught was nurses, followed by physicians. Most sources did not report using an information literacy framework or instructional design model, undertaking needs assessments, or reporting formal objectives or assessment. Conclusion: While HSLs are active supporters of EBP, we need to apply the same principles to our own professional practice. Formal structure of programming and program assessment combined with clear, detailed reporting can help to build a more robust evidence base to support future CE provision.
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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 | 0.038 | 0.157 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.031 | 0.031 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| 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, 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".