Disseminating the lessons of evidence-based practice
Bibliographic record
Abstract
Introduction Dissemination of research results is vital to the progress of the profession as well as helping to improve practice. It involves not only making your research available, but also ensuring that it is accessible to others and presented in a manner that is easy to understand. In addition, it is important to use a variety of techniques for delivering information in order to provide evidence (or to conduct research where no data are available) for what we do as librarians and to help others understand how we define our role. In this chapter, we explore the evidence for dissemination and the different methods by which research and knowledge can be circulated. We also provide an overview of how distribution is used by librarians and investigate innovative ways to make your research known. Two types of disseminators, those who conduct research and those who use research findings in their practice, are examined throughout the chapter. Scenario As a special librarian, you work with a business research group consisting of a statistician, a research assistant and an MBA. Your role includes conducting environmental scans, facilitating access to online resources and performing comprehensive literature searches. You are also being asked to order documents for employees and other time-consuming tasks which could be done by other staff. You report to the vice president of the organization, who understands little about your role, yet she speaks on your behalf at board meetings. Since you see your boss infrequently, you prepare carefully for your meetings. You wish to communicate your needs and wants better, using evidence. You also want to find effective ways to get vital information to your supervisor. As a solo librarian, you also worry that you are isolated from other librarians and want to find ways to keep up with issues of relevance to your profession while at the same time gaining the support of your colleagues. Effective dissemination Dissemination continues to be a hot topic in recent literature. In order to be effective, research needs to be user-friendly, that is, understandable by those to whom we report, as well as to our colleagues. Many libraries are run by non-librarians and many librarians report to a person in another discipline who may not clearly understand their role and function within the organization.
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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 | 0.018 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".