Evidence-Based Practice in PubMed: Are Shared Filters Useful to Health Sciences Academic Users?
Bibliographic record
Abstract
Objectives In April 2010, the Université de Montréal’s Health Sciences Library has implemented shared filters in its institutional PubMed account. Most of these filters are designed to highlight resources for evidence-based practice, such as Clinical Queries, Systematic Reviews and Evidence-based Synopsis. We now want to measure how those filters are perceived and used by our users. Methods For one month, data was gathered through an online questionnaire proposed to users of Université de Montréal’s PubMed account. A print version was also distributed to participants in information literacy workshops given by the health sciences librarians. Respondents were restricted to users affiliated to Université de Montréal’s faculties of Medicine, Dentistry, Veterinary Sciences, Nursing and Pharmacy. Basic user information such as year/program of study or department affiliation was also collected. The questionnaire allowed users to identify the filters they use, assess the relevance of filters, and also suggest new ones. Results Survey results showed that the shared filters of Université de Montreal’s PubMed account were found useful by the majority of respondents. Filters allowing rapid access to secondary resources ranked among the most relevant (Reviews, Systematic Reviews, Cochrane Database of Systematic Reviews, Practice Guidelines and Clinical Evidence). For Clinical Study Queries, Randomized Controlled Trial (Therapy/Narrow) was considered the most useful. Some new shared filters have been suggested by respondents. Finally, 18% of the respondents indicated that they did not quite understand the relevance of filters. Conclusion Based on the survey results, shared filters considered most useful will be kept, some will be enhanced and others removed so that suggested ones could be added. The fact that some respondents did not understand well the relevance of filters could potentially be addressed through our PubMed workshops, online library guides or by renaming some filters in a more meaningful way.
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 | 0.201 | 0.621 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".