Evaluating the Usability, Perceived Performance, and Perceived Effects of KBGAN iHealth© and KBGAN iFeed© Mobile Apps for Buffalo Management in Selected Municipalities in the Philippines
Bibliographic record
Abstract
This study evaluates the KBGAN iHealth© and KBGAN iFeed© mobile apps designed for buffalo health and feeding management, particularly for agricultural extension professionals (AEPs) in selected Philippine municipalities. These apps aim to address challenges in buffalo management, such as limited access to veterinary expertise, personalized recommendations, organized data, communication channels, and difficulties in calculating ideal feed compositions and meeting the distinct needs of smallholder farmers and AEPs. Despite System Usability Scale (SUS) scores indicating marginal acceptability for both apps, weighted mean scores by AEPs for statements assessed on a 5-point Likert scale (1 as strongly disagree and 5 as strongly agree), demonstrate that AEPs reported high confidence in the accuracy of buffalo health diagnostics (Mean of 4.20) and health management recommendations (Mean of 4.17) provided by KBGAN iHealth©. Similarly, KBGAN iFeed© received favorable ratings, with AEPs expressing agreement on the accuracy of feeding recommendations (Mean of 3.89) and the facilitation of feeding ration computations (Mean of 4.00). These positive perceived performance outcomes, coupled with increased confidence (Mean of 4.03) and motivation (Mean of 4.00) among AEPs, suggest the potential for frequent and consistent app usage despite usability concerns. Chi-square tests examining the relationship between AEP characteristics and SUS scores for the apps reveal significant associations between AEPs' education and experience levels and SUS scores for KBGAN iFeed©. The choice of IT device also influences KBGAN iHealth© SUS scores. Proposed enhancements by AEPs include refining algorithms, improving the user interface for navigation, speed, and efficiency, and incorporating features such as photo uploads and geotagging.
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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 | 0.015 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".