Creating a mobile subject guide to improve access to point-of-care resources for medical students: a case study
Bibliographic record
Abstract
QUESTION: Can a mobile optimized subject guide facilitate medical student access to mobile point-of-care tools? SETTING: The guide was created at a library at a research-intensive university with six teaching hospital sites. OBJECTIVES: The team created a guide facilitating medical student access to point-of-care tools directly on mobile devices to provide information allowing them to access and set up resources with little assistance. METHODS: Two librarians designed a mobile optimized subject guide for medicine and conducted a survey to test its usefulness. RESULTS: Web analytics and survey results demonstrate that the guide is used and the students are satisfied. CONCLUSION: The library will continue to use the subject guide as its primary means of supporting mobile devices. It remains to be seen if the mobile guide facilitates access for those who do not need assistance and want direct access to the resources. Internet access in the hospitals remains an issue.
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How this classification was reachedexpand
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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".