Qualitative evaluation by community health professionals of a technology design for recording and mobilizing patient reported care narratives
Bibliographic record
Abstract
Introduction: Gaps in the health system make it difficult for older adults to have their communication needs and wants heard for that necessary communication to happen. In this study, we investigate attitudes of community-based healthcare professionals (HCPs) regarding the potential utility of a digital technology platform. This platform was designed to enable older adults receiving HCP services in a community setting to provide qualitative narrative impressions and evaluations of their needs and how these needs were addressed by the services. MyHealthMyRecord (MHMR) technology is being designed to provide a digital video-scrapbook for recording and sharing healthcare-related information, observation, and qualitative narratives. Methods: HCPs were recruited in collaboration with SE Health, a large community-care provider based in Toronto, Canada. Using a user-centered and inclusive design approach, HCPs in community care were asked to comment on the use of the MHMR platform by their patients and to examine a platform prototype. Each session, which lasted approximately 60 min, involved a 40-min semistructured face-to-face interview, followed by a demonstration of the system. Data were analyzed using the Consensual Qualitative Research analysis. Results: A convenience sample of 7 HCPs, all comfortable with digital technology, participated in the study. The analysis identified 11 themes, with all participants recognizing the value of the MHMR platform in enhancing their capacity to provide services, particularly in tracking to relevant events between patients' visits. Conclusions: The HCPs appreciated how the MHMR platform could be used to enhance community health service delivery through valid, reliable, and responsive Person/Patient-Reported Outcomes. It was appreciated that such data could be useful in improving community care experiences while also being useful at a system-wide learning level.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".