Creating and Assessing a Subject-based Blog for Current Awareness within a Cancer Care Environment
Bibliographic record
Abstract
The Health Information Network Calgary (HINC) is comprised of a group of libraries providing information services and resources to urban and rural sites in the Calgary Zone of Alberta Health Services. Establishing a current awareness service is a necessity in any discipline, especially in health care. Web 2.0 and social networks have transformed how health care professionals and researchers create knowledge, access information, collaborate, and disseminate research. One of the earliest forms of social media, blogging has taken the world by storm (1) . Although there is a wealth of literature on the use of blogs in providing current awareness services for libraries, there is a pronounced gap on how blogs are assessed or evaluated, especially for information alert purposes (2) . Clients within the HINC subscribe to e-mail alerts and RSS feeds, a trend particularly evident within the Cancer Care environment where a number of researchers have already implemented feed readers to remain aware of current literature. However, they often comment on challenges associated not only with maintaining alerts and managing RSS feeds, but also in selecting and creating alerts for unpublished materials. The need for a librarian-facilitated current awareness strategy became more and more apparent. The literature reviewed addressed the value of an alert, namely to indicate a gap in the participant‘s knowledge, rather than to deliver content the librarians may have perceived as useful (3). The authors saw the creation of a subject-based blog as an opportunity to disseminate current awareness “grey” information to this specific research community. The Grey Horizon Blog was created in April 2012 using Blogger. The selection and re-aggregation of information involves ongoing assessment of user needs and continuous work on the Blog. A weekly global email-digest listing of the postings will be distributed two months after the launch. Several metrics will be employed in October 2012 to evaluate the Blog. Blogger itself tracks the number of page-views over time. Google Analytics was set up as it tracks additional information on access and use of the Blog. As clients may be using feed readers to read Blog entries and may thus not visit the Blog at all, Feedburner has also been incorporated to track the number of times that the Blog RSS is accessed, as well as calculating the number of subscribers. A post-survey will be conducted in six months to complement the web statistics data. The additional feedback and comments will help us determine whether the Blog has successfully created an easy platform for users to keep current with unpublished literature, the type of resources found most important, and whether the amount of time spent maintaining the Blog met expectations. It is anticipated that this case study will portray how to successfully plan a subject-based blog to meet users’ current awareness information needs in grey literature. Further efforts will focus on targeting the Blog to the topic areas in grey literature where users feel more information is needed. The findings from this assessment will direct us to potential marketing opportunities and changing technology that haven’t been fully utilized in our Grey Horizon Blog.
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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 | 0.027 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".