Relevance of a French National Database Dedicated to Infection Prevention and Control (NosoBase®): A Three-Step Quality Evaluation of a Specialized Bibliographic Database
Bibliographic record
Abstract
Abstract Objective – NosoBase® is a collection of documentation centres with a national bibliographic database dedicated to infection prevention and control (IPC), with over 20 years of experience in France. As a quality assurance activity, this study was conducted in 2017 with a three-step approach to evaluate the bibliographic database regarding (1) the availability and coverage of citations; (2) the scope and relevance of content; and (3) the quality of the documentation centre services. Methods – The three-step quality approach involved (1) evaluating the availability and coverage of citations in NosoBase® by searching for the bibliographic citations of three systematic reviews on hand hygiene practices, published recently in three different peer-reviewed international journals; (2) evaluating the scope and relevance of content in NosoBase® by searching for all documents from 2015 indexed in NosoBase® under hand hygiene related keywords, and analyzing according to publication language, document type (e.g., legislation, research, or guidelines), and target audience; and 3) evaluating the strengths, weaknesses, and opportunities of the documentation centre services, with interviews involving the librarians. Results – NosoBase® contained 70.8%-80.9% of references directly concerning hand hygiene cited by the three systematic reviews. Of the 200 articles indexed in NosoBase® under hand hygiene related keywords in 2015, 22.5% were French language based, with a significant representation of French non-indexed literature. The analysis of the documentation centre services highlighted future opportunities for growth, building on the strengths of experience and collaborations, to improve marketing and usability, targeting francophone IPC professionals. Conclusion – Specialized bibliographic databases may be useful and time efficient for the retrieval of relevant specialized content. NosoBase® has significant relevance to French and francophone healthcare professionals in its representation of French documentation and healthcare literature not otherwise indexed internationally. NosoBase® needs to highlight its resources and adapt its services to allow easier access to its content.
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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 | 0.427 | 0.656 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.050 | 0.044 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".