Strategies to use tablet computers for collection of electronic patient-reported outcomes
Bibliographic record
Abstract
BACKGROUND: Mobile devices are increasingly being used for data collection in research. However, many researchers do not have experience in collecting data electronically. Hence, the purpose of this short report was to identify issues that emerged in a study that incorporated electronic capture of patient-reported outcomes in clinical settings, and strategies used to address the issues. FINDINGS: The issues pertaining to electronic patient-reported outcome data collection were captured qualitatively during a study on use of electronic patient-reported outcomes in two home dialysis units. Fifty-six patients completed three surveys on tablet computers, including the Kidney Disease Quality of Life-36, the Edmonton Symptom Assessment Scale, and a satisfaction measure. Issues that arose throughout the research process were recorded during ethics reviews, implementation process, and data collection. Four core issues emerged including logistics of technology, security, institutional and financial support, and electronic design. CONCLUSIONS: Although use of mobile devices for data collection has many benefits, it also poses new challenges for researchers. Advance consideration of possible issues that emerge in the process, and strategies that can help address these issues, may prevent disruption and enhance validity of findings.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".