Informing the development of a digital health platform through Universal Points of Care: qualitative survey
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
Background: Epilepsy, multiple sclerosis (MS) and depression are chronic conditions where technology holds potential in clinical monitoring and self-management. Over five years, the RADAR-21 CNS consortium is exploring the application of remote measurement technology (RMT) to the management and self-management of patients in these clinical areas. The consortium is large and includes clinical and non-clinical researchers as well as a patient advisory board. Objective: A formative development study was conducted to understand how consortium members viewed the potential of RMT in these conditions. Methods: In this qualitative survey study, we developed a methodological tool, Universal Points of Care (UPOC), to gather views on the potential use, acceptance and value of a novel Remote Measurement Technology (RMT) platform across three chronic conditions (MS, epilepsy and depression). UPOC builds upon use case scenario methodology, utilising expert elicitation and analysis of care pathways to develop scenarios applicable across multiple conditions. After developing scenarios, we elicited views on the potential of RMT in these different scenarios through a survey administered to 28 subject matter experts, consisting of 16 healthcare practitioners, 5 33 healthcare services researchers, and 7 people with lived experience of MS, epilepsy or depression. Survey results were analysed thematically and using an existing framework of factors describing links between design and context. Results: The survey elicited potential beneficial applications of the RADAR-CNS RMT system, as well as patient, clinical and non-clinical requirements of RMT across the three conditions of interest.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it