“It's not just teaching for the sake of teaching anymore”
Bibliographic record
Abstract
Medical libraries have had to swiftly change the way they connected their clients to instruction services starting Spring of 2020. The focus of this article is on the instructional services of [hospital library] pre and post 2020 and it will also provide insights into what hospital libraries in Canada have been doing, what they are succeeded at and working on. This paper will describe the design, implementation, and outcomes of a Quality Improvement (QI) project and concludes by exploring the changes recommended to enhance online instructional services in an urban research and teaching hospital library environment. The QI project led the team to prioritize three areas for improvement: a peer feedback system that has been designed to support instructors in building their confidence and skills; monthly the Instruction Team will debrief any workshops using the peer feedback data, attendance information, instructor feedback, and attendee evaluations. The workshops offered will expand beyond the typical hour-long, one-off workshop sessions to other formats. The results of the QI process have produced many ideas that could be used to improve the design and delivery of online instruction and confirmed that libraries in hospitals across Canada are encountering similar barriers around time, staffing capacity, technology, and training.
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.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".