“We want it now and we want it easy”: Usability testing of an online health library for healthcare practitioners
Bibliographic record
Abstract
Introduction – The purpose of this study was to undertake website usability testing of the Saskatchewan Health Information Resources Partnership (SHIRP) online library website,. a digital library for healthcare providers working in the province of Saskatchewan, to determine whether the SHIRP website is intuitive for healthcare practitioners to use. Methods: Thirteen volunteers from four locations in the province participated in a usability test that included a portion devoted to the completion of tasks, as well as a series of semi-structured interview questions. Data were analyzed and themes were identified that were used to redesign the SHIRP website. Results – Nine out of the 13 main menu terms on the SHIRP website were problematic. A relatively low number of participants completed the assigned tasks on the first try. The SHIRP website was determined to be unwieldy and not completely intuitive. Conclusions – Asking front line healthcare providers what they need and want in an online library website should be the first step in creating or redesigning such a site. The time available to healthcare providers for doing library research is often limited, so the site needs to be simple, clean, and fast to use.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".