Using Social Media to Create Virtual Interest Groups in Hospital Libraries
Bibliographic record
Abstract
Social networking has positively impacted the realm of human interactivity. Although libraries have traditionally been viewed as a place for research and information seeking, Web 2.0 technologies, such as blogs and Instant Messaging (IM), are starting to change users’ experiences of current library services. In this study we aim to examine whether the opportunity for enhanced researcher-librarian interaction that Web 2.0 tools provide creates a synergistic experience for health research interest groups. In addition, we will explore whether these tools increase efficiency in obtaining information and/or improve quality and quantity of research evidence. This study was conducted by two hospital librarians working in tertiary hospitals. An email was distributed to the health care professionals from these two sites, inviting participation in two online research interest groups: Clinical Practice Guidelines and Patient Safety. A pre-survey of participants was intended to assess the comfort level of this group with Web 2.0 tools and to gauge their level of use both professionally and personally. We created a Virtual Interest Group (VIG) environment to incorporate the following Web 2.0 tools into the existing library website: user blogs, enabling commenting to facilitate a knowledge-sharing atmosphere; chat software to assist with easy access to acquired information; and Delicious tagging for a more systematic documentation of grey literature. A post-survey was conducted three months later in order to re-evaluate the participants’ experiences with social media, in particular with the online interest group environment. Findings from this study can be used to highlight future trends around the discoverability of grey literature with social media tools and to establish a basis for integrating Web 2.0 tools in library websites and services.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".