Evaluation of three point-of-care healthcare databases: BMJ Point-of-Care, Clin-eguide and Nursing Reference Centre
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it