Selecting Which Databases to Teach Students in Communication Disorders by Considering Database Pairs that Index Core Journals in the Field
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A Review of:
 Grabowsky, A. (2015). Library instruction in communication disorders: Which databases should be prioritized? Issues in Science and Technology Librarianship 79(Winter). doi:10.5062/F4707ZFB
 
 Abstract
 
 Objective – There are two objectives in this research article. The first is to identify databases that librarians usually recommend to students for searching topics in communication disorders. The second is to compare these databases’ indexing of core journals in communication disorders, with the purpose of ascertaining which databases should be taught first in a one-shot information literacy session.
 
 Design – A comparative database evaluation using citation analysis.
 
 Setting – 10 universities in the United States of America offering LibGuides for their audiology or speech language pathology programs. 
 
 Subjects – Six databases: CINAHL, ERIC, Linguistics and Language Behavior Abstracts (LLBA), PsycINFO, PubMed/Medline, and Web of Science/Web of Knowledge.
 
 Methods – The author selected 10 universities from the top 20 included in the graduate school rankings for audiology and/or speech language pathology from U.S. News & World Report. The 10 universities selected were chosen because their librarians published online subject guides using LibGuides that suggest databases students can use for searching topics in communication disorders. The LibGuides were then examined to identify the most popular recommended databases that the author subsequently used for comparing coverage of core journals in communication disorders. The author generated a core journals list by selecting the top 20 audiology and speech-language pathology journals from Journal Citation Reports, SCImago Journal & Country Rank, and Google Scholar Top Publications. These three sources produced lists of influential journals in different subject areas by looking at the number of citations the journals have received, alongside other factors. The author searched for 33 journals in total in each of the subject databases previously identified. 
 
 Main Results – The author found six databases that were mentioned in the LibGuides of at least half the universities investigated. None of the 6 databases indexed all 33 core journals. The breakdown of the number of journals indexed in each database is as follows: Web of Science/Web of Knowledge indexed 32 of 33 core journals (97%); PubMed/Medline indexed 28 (85%); PsycINFO indexed 27 (82%); CINAHL indexed 25 (76%); LLBA indexed 23 (70%); and ERIC indexed 9 journals (27%).
 
 Conclusion – The author discovered that pairing certain databases allows for coverage of all 33 core journals. These pairings are: PubMed/Medline with PsycINFO, PubMed/Medline with LLBA, PubMed/Medline with Web of Science, Web of Science with PsycINFO, and Web of Science with LLBA. The author suggests that librarians can create instructional materials for all recommended databases, “but use information from this study together with institution-specific factors to decide which databases to prioritize in face-to-face instruction sessions for speech-language pathology and audiology students” (Conclusion).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.153 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it