Evaluating clinicians’ user experience and acceptability of LearnTB, a smartphone application for tuberculosis in India
Bibliographic record
Abstract
Background: Tuberculosis (TB) is the leading infectious killer, and India accounts for 2.8 of the 10.4 million TB cases that occur each year, making it the highest TB burden country worldwide. Poor quality of TB care is a major driver of the epidemic in India. India’s large private, unregulated sector manages over 50% of the TB patients, with studies showing suboptimal diagnosis and treatment in the private sector. Better education of doctors using mobile applications (apps) is a possible solution. While India has seen an explosion of mobile phone services, and while the use of mobile health interventions has been gaining interest, little is known about mHealth around tuberculosis in India. Methods: Our study aimed to understand the user experience and acceptability of a smartphone application, LearnTB, amongst private sector academic clinicians in India. This study was conducted amongst 101 clinicians at Kasturba Hospital, Manipal, India. The user experience of participants (part 1) and acceptability (part 2) were evaluated with the use of two valid, English, paper-based questionnaires. The first questionnaire was based on the System Usability Scale (SUS); the second questionnaire was based on the Technology Acceptance Model (TAM). Data were collected during February and March 2017 and were analyzed using descriptive statistics, multiple linear regression as well as logistic regression analysis. Results: A response rate of 99% was achieved; 100 participants responded to the second questionnaire and 100% of the participants responded to the first questionnaire. User experience was very high [mean SUS score =94.4 (92.07–96.76)]. Perceived usefulness (PU) was significantly correlated to intention to use (IU) (r=0.707, P<0.0001), and perceived ease of use (PEU) was significantly correlated to PU (r=0.466, P<0.0001). Path analysis confirmed the direct relationship between PU and IU (0.936, P<0.0001), and the indirect relationship between PEU and IU (0.5102, P<0.0001). Logistic regression analysis helped target items strongly influencing IU, such as “The use of the LearnTB application is compatible with my work habits” [OR =3.20 (1.04–9.84), P=0.004] and “The use of the LearnTB application could promote good clinical practice” [OR =5.23 (1.35–20.29); P=0.016]. Conclusion: The first part of the study indicated high user experience of the LearnTB application. The TAM questionnaire (second part) explained a significant portion of the variance in clinicians’ IU the LearnTB application. The PU of the application has the highest impact on the clinicians’ IU the Learn TB application. This study provides a preliminary analysis of mobile health interventions for tuberculosis in India, and emphasizes the need for future research in this domain.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".