Usability Analysis of a Health Sciences Digital Library by Medical Residents: Cross-sectional Survey
Bibliographic record
Abstract
BACKGROUND: The usability of a digital library depends on a myriad of factors ranging from the end users' ability to website complexity. Although digital libraries provide instant access to online content, offering an efficient reference platform, their usability is highly variable. OBJECTIVE: The aim of this study was to measure users' perspectives and usability of the digital library of the Saudi Commission for Health Specialties (SCFHS). METHODS: A web-based questionnaire survey was conducted using a validated System Usability Scale (SUS) containing 5 positive and 5 negative items on the usability of the digital library. The SUS standard cut-off score of 68 was considered for interpretation. RESULTS: The overall mean SUS score of digital library usability was 52.9 (SD 15.2) with a grade "D" categorization, indicating low usability. The perceived measures of attributes of the 10 SUS items of findability, complexity, consistency, and confidence obtained below average scores. Only item 1 relating to perceived willingness to use the digital library frequently obtained a score above the targeted benchmark score (mean score 3.6). Higher SUS scores were associated with training (P=.02). Men felt the digital library to be more complex (P=.04) and board-certified physicians perceived a greater need for training on digital library use (P=.05). Only the UpToDate database was widely used (72/90, 80%). CONCLUSIONS: These findings demonstrate the low usability of the extensive facilities offered by the SCFHS digital library. It is pivotal to improve awareness of the availability of the digital library and popularize the databases. There is also a need for improved user training to enhance the accessibility and usability of the multiple databases.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".