Evaluation of three point-of-care healthcare databases: BMJ Point-of-Care, Clin-eguide and Nursing Reference Centre
Bibliographic record
Abstract
BACKGROUND: Point of care resources make it easier for clinicians to find answers to questions that arise during a clinical encounter. In order to make informed purchase decisions in times of tight budgets, librarians need to have a better understanding of which resources will meet their patrons' clinical information needs. OBJECTIVES: The goal of this study was to assess the content, interface and usability of three point-of-care tools: BMJ Point-of-Care, Clin-eguide and Nursing Reference Centre. METHODS: A questionnaire designed to gather quantitative and qualitative data was created using Survey Monkey. The survey was distributed to healthcare practitioners in Alberta's two largest health regions, and the data were analysed for emergent themes. RESULTS: The themes that arose--ease of use, validated content, relevancy to practice--generally echoed those stated in the literature. No one database fared significantly better, due to differing features, content and client preference. CONCLUSIONS: Despite the limitations of the survey, the themes that emerged provide a springboard for future research on the efficacy of information resources used at the point of care, and the need for deeper analysis of these recent additions to the medical information market.
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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 | 0.067 | 0.235 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".