Informing Evidence Based Decisions: Usage Statistics for Online Journal Databases
Bibliographic record
Abstract
Abstract Objective – The primary objective was to examine online journal database usage statistics for a provincial ministry of health in the context of evidence based decision-making. In addition, the study highlights implementation of the Journal Access Centre (JAC) that is housed and powered by the Ontario Ministry of Health and Long-Term Care (MOHLTC) to inform health systems policy-making. Methods – This was a prospective case study using descriptive analysis of the JAC usage statistics of journal articles from January 2009 to September 2013. Results – JAC enables ministry employees to access approximately 12,000 journals with full-text articles. JAC usage statistics for the 2011-2012 calendar years demonstrate a steady level of activity in terms of searches, with monthly averages of 5,129. In 2009-2013, a total of 4,759 journal titles were accessed including 1,675 journals with full-text. Usage statistics demonstrate that the actual consumption was over 12,790 full-text downloaded articles or approximately 2,700 articles annually. Conclusion – JAC’s steady level of activities, revealed by the study, reflects continuous demand for JAC services and products. It testifies that access to online journal databases has become part of routine government knowledge management processes. MOHLTC’s broad area of responsibilities with dynamically changing priorities translates into the diverse information needs of its employees and a large set of required journals. Usage statistics indicate that MOHLTC information needs cannot be mapped to a reasonably compact set of “core” journals with a subsequent subscription to those.
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 | 0.083 | 0.458 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.020 | 0.032 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".