Accessibility, Relevance, and Impact of a Symptom Monitoring Tool for Home Hospice Care: Theory Elaboration and Qualitative Assessment
Bibliographic record
Abstract
BACKGROUND: Early users found Engagement and Visualization to Improve Symptoms in Oncology Care (ENVISION), a web-based application designed to improve home management of hospice patients' symptoms and support patients' and family caregivers' well-being, to be generally useful and easy to use. However, they also raised concerns about potential challenges users with limited technological proficiency might experience. OBJECTIVE: We sought to concurrently accomplish two interrelated study aims: (1) to develop a conceptual framework of digital inclusivity for health information systems and (2) to apply the framework in evaluating the digital inclusivity of the ENVISION application. METHODS: We engaged ENVISION users (N=34) in a qualitative study in which data were collected via direct observation, think-aloud techniques, and responses to open-ended queries. Data were analyzed via theory elaboration and basic qualitative description. RESULTS: Accessibility, relevance, and impact were identified as 3 essential considerations in evaluating a health system's digital inclusivity. Study findings generally supported ENVISION's digital inclusivity, particularly concerning its perceived relevance to the work of family caregivers and hospice clinicians and its potentially positive impact on symptom management and quality of life. Limitations to ENVISION's digital inclusivity centered around issues of accessibility, particularly operability among individuals with limited technological knowledge and skills. CONCLUSIONS: The Accessibility, Relevance, and Impact conceptual framework of digital inclusivity for health information systems can help identify opportunities to strengthen the digital inclusivity of tools, such as ENVISION, intended for use by a broad and diverse range of users.
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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 | 0.067 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".