Apps and Mobile Support Services in Canadian Academic Medical Libraries
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
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.
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 | 0.006 | 0.002 |
| 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 | 0.002 | 0.004 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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