Using “USEtool”: Usability Evaluation Method for Quality Architecture in-Use
Bibliographic record
Abstract
The main priority of Malaysian healthcare design quality is to organize an informational domain of a patient-oriented care design by patient experience to usable environment. The usability evaluation is an appropriate qualitative research design dealing with a process concerning the understanding of the user and context of use. This paper provides strategies for evaluating quality architecture in use from user experience and approaches for analyses of applicable qualitative data. Case studies have been conducted to explore the usability of three replacement hospitals in Peninsular Malaysia using “USEtool” evaluation method introduced by Hansen, Blakstad, and Knudsen (2011). It is a five-stage evaluation process focusing on the following questions: for what, what, where and whom, why, and lastly, the final report as an action plan and input for the improvement of a building quality environment design in use. The process of data analysis is based on thematic analysis principles using NVivo 9. The findings indicate that (1) the quality of care is the positive users’ experience feedback on the usability of physical environment design that fulfils their needs and expectations, (2) there is a strong relationship between the usability physical environment criteria and overall patient satisfaction, and (3) the usability evaluation is useful for benchmarking and creating comparative databases, which optimally accommodates the needs of users and acts as a learning feature for improving the existing or future design.
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 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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".