Usability Evaluation of a Portable Health Information Kiosk Using a SMAARTTM Intervention Framework
Bibliographic record
Abstract
Empirical literature has shown that interventions to address social determinants of health are limited owing to poor integration of social and clinical data. The objective of this study was to describe a Sustainable, Multisector, Accessible, Affordable, Reimbursable, and Tailored framework (SMAARTTM) which was utilized to design and pilot test portable health information kiosk that can facilitate the integration of social determinants of health data with clinical data to enhance population health outcomes in global settings. The SMAART TM framework was designed using a combined approach of Data, Information Knowledge, Human Centered approach and behavioral humanistic and learning theories, and was applied to develop and evaluate an interactive, bi-lingual computer enabled portable health information kiosk. A convenience sample (recruitment based on accessibility to the researcher) of 149 individuals aged 18 years and above living in urban slum settings of India were enrolled in the year 2013. Subjective and objective data gathering included socio-demographics, clinical history, health behaviors and knowledge, attitude and practices. Weight and blood pressure levels were measured using physiological sensors. Usability assessment of the health information kiosk was also conducted. Results showed an increased burden of chronic non-communicable disease (NCD) risk factors and related knowledge, and lack of healthy lifestyle practices among urban slum individuals. Our study showed that the technology enabled SMAART TM framework can be utilized to develop an individual risk profile for better disease prevention, monitoring and management of chronic NCDs.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".