Examining the Use of an Open Digital Health Library for Professionals
Bibliographic record
Abstract
BACKGROUND: The Norwegian Electronic Health Library (The Library) is a website for health personnel. Most of the content is also open to the public. Usage statistics have risen sharply in the years 2010-2013. OBJECTIVE: We wanted to find out whether the rise was caused by health personnel, the general public, or other factors. METHODS: Since we lacked direct information, we had to use proxy data to shed light on our questions. We applied mixed methods (database of registered users, user survey, usage statistics, and statistics from suppliers), and triangulated between them. RESULTS: Health personnel were our largest user group, but The Library was also accessed by students, patients, and other groups. Content in Norwegian was preferred to English language content. Concise, practical information was preferred to more comprehensive information. Patient leaflets were the most popular information type. Mobile phone visits differed from personal computer visits both in terms of time of day and what kind of information was viewed. CONCLUSIONS: The Library was used mostly by health personnel, as intended, but our data are inconclusive regarding a possible change in user groups. There was a large degree of consistency in results when using different investigation methods. The survey points toward health personnel being the largest user group, and the usage statistics show that patient leaflets are the most popular content, being viewed by both health personnel and patients.
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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 | 0.012 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".