Using Smartphone-Based Ecological Momentary Assessment in Audiology Research: The Participants' Perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
= 10) participated in a study using a smartphone-based EMA system to measure their auditory lifestyles. A 14-item survey was scheduled to deliver every 45 min by an EMA app. After a 1-week trial, participants were interviewed regarding their study experiences. The app log files were analyzed to understand how the participants interacted with the app. Results Across the two groups, 1,295 surveys were completed (compliance rate 74.4%). On average, HI participants completed 10.0 and NH participants completed 9.1 surveys per day. The mean survey completion time for HI and NH groups were 72 s and 51 s, respectively. For both groups, about 90% of the participants reported the app as easy to use; about 60% of the participants reported that repetitive surveys interrupted or somewhat interrupted their activities. Participants reported surveys disrupting situations, for example, working, driving, and social events, and that they were more likely to skip surveys in these situations. Additionally, 50% of NH and 30% of HI participants indicated that the survey was not delivered too frequently and none indicated that the survey was too long. Conclusion Overall, the app and EMA design seem to be appropriate. Insights from this study can help researchers design their studies to adequately assess listeners' experience in the field with optimal compliance and data quality.
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it