Bibliographic record
Abstract
Objective: To examine how Canadian academic medical libraries are supporting mobile apps, what apps are currently being provided by these libraries, and what types of promotion are being used. Methods: A survey of the library websites for the 17 medical schools in Canada was completed. For each library website surveyed, the medical apps listed on the website, any services mentioned through this medium, and any type of app promotion events were noted. When Facebook and Twitter accounts were evident, the tweets were searched and the past two years of Facebook posts scanned for mention of medical apps or mobile services/events. Results: All seventeen academic medical libraries had lists of mobile medical apps with a large range in the number of medical relevant apps (average=31, median= 23). A total of 275 different apps were noted and the apps covered a wide range of subjects. Five of the 14 Facebook accounts scanned had posts about medical apps in the past two years while 11 of the 15 Twitter accounts had tweets about medical apps. Social media was only one of the many promotional methods noted. Outside of the app lists and mobile resources guides, Canadian academic medical libraries are providing workshops, presentations, and drop-in sessions for mobile medical apps. Conclusion: While librarians cannot simply compare mobile services and resources between academic medical libraries without factoring in a number of other circumstances, librarians can learn from mobile resources strategies employed at other libraries, such as using research guides to increase medical app literacy.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".