Domains of professional practice: analysis of publications in the Journal of the Medical Library Association from 2010 to 2019
Bibliographic record
Abstract
The Medical Library Association (MLA) has defined 7 domain hubs aligning to different areas of information professional practice. To assess the extent to which content in the Journal of the Medical Library Association (JMLA) is reflective of these domains, we analyzed the magnitude of JMLA articles aligning to each domain hub over the last 10 years. Bibliographic records for 453 articles published in JMLA from 2010 to 2019 were downloaded from Web of Science and screened using Covidence software. Thirteen articles were excluded during the title and abstract review because they failed to meet the inclusion criteria, resulting in articles included in this review. The title and abstract of each article were screened by two reviewers, each of whom assigned the article up to two tags corresponding to MLA domain hubs (i.e., information services, information management, education, professionalism and leadership, innovation and research practice, clinical support, and health equity & global health). These results inform the MLA community about our strengths in health information professional practice as reflected by articles published in JMLA.
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.
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 | 0.017 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.062 | 0.068 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".