Developing an Android-Based Patient Decision Aid Based on Ottawa Standards for Patients After Kidney Transplant and Its Usability Evaluation.
Bibliographic record
Abstract
OBJECTIVES: This study was conducted to develop an android based patient decision aid (PDA) as a self-care instrument for patients after kidney transplant and its usability evaluation. METHODS: In this study, the systematic development process of Android-based self-care application for patients after kidney transplant based on Ottawa standard was included: scoping, assemble steering group, analysis of requirements, designing, develop of a prototype and system evaluation. The PDA is a self-triage system that will help early identification of risk symptoms in patients, and help manage them. System recommendations for risk signs are: Refer to the nearest hospital or healthcare center without delay, refer to the doctor and tell your doctor in the next visit. To identify patient care needs, a semi-structured interview with members of steering group, including patients and clinical experts, was conducted by the researchers. A prototype of the decision aid was made according to identified needs in the previous step. Finally, in order to evaluate its usability rate by using the System Usability Scale (SUS) questionnaire, it was used by exerts and patients. RESULTS: This study identified information needs, risk signs and steps that patients need to make appropriate decisions about them. The main capabilities of the decision aid are features such as reminders for appointment/test, time of taking medication, registration of symptoms, weight, blood pressure, body temperature, advising to patient in case of signs of risk, weight, blood pressure, body temperature and test results which were reported in the diagram. The mean score of system's usability evaluated by medical informatics specialists, clinicians, and patients were 88.33, 95, and 91. CONCLUSIONS: PDAs was usable and desirable from the point of view of medical informatics specialists, clinicians and patients.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".