Tiny? Make it mighty! Maximizing a limited-budget upgrade of a pint-sized hospital library using UX methods
Bibliographic record
Abstract
Introduction: The University of Ottawa Heart Institute's Berkman Library space is outdated. Budget constraints and tiny square footage leave little room for error. A needs assessment using user experience (UX) research methods was conducted from 2022 to 2023 to inform strategic decisions on updating and reorganizing furnishings to better support library patrons and their needs. Methods: Data was collected via an electronic survey, "guerilla" interviews, observations of library patrons, and a physical survey of communal spaces in the building. Resulting qualitative data were compiled and examined for common themes. Low fidelity mockups of furnishings and space arrangements were prototyped and presented to patrons for feedback. Results: Quiet was one of the most valued attributes of the library space and showed itself to be a unique quality of the library when compared to communal spaces within the hospital. Survey and interview responses consistently cited soft, comfortable furnishings as desirable additions. Observed behaviours support the continued need for desks with a deep surface area to accommodate multiple devices used in tandem. Flexible use of computer hardware, better access to power outlets, and adjustable lighting were identified as additional gaps. Discussion: Methods showcase light-weight space assessment strategies that are of particular interest to solo librarians or small library teams working in a hospital environment. Results identify library qualities that address institutional gaps and provide insight into the motivators, needs, and behaviours of hospital staff. Centering patron behaviours and preferences in the project's methodology provides data to support decision-making for near term upgrades and long-term library policy.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".