Qualitative study to elicit patients’ and primary care physicians’ perspectives on the use of a self-management mobile health application for knee osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: To elicit perspectives of family physicians and patients with knee osteoarthritis (KOA) on KOA, its treatment/management and the use of a mobile health application (app) to help patients self-manage their KOA. DESIGN: A qualitative study using Cognitive Task Analysis for physician interviews and peer-to-peer semistructured interviews for patients according to the Patient and Community Engagement Research (PaCER) method. SETTING: Primary care practices and patient researchers at an academic centre in Southern Alberta. PARTICIPANTS: Intentional sampling of family physicians (n=4; 75% women) and patients with KOA who had taken part in previous PaCER studies and had experienced knee pain on most days of the month at any time in the past (n=5; 60% women). RESULTS: Physician and patient views about KOA were starkly contrasting. Patient participants expressed that KOA seriously impacted their lives and lifestyles, and they wanted their knee pain to be considered as important as other health problems. In contrast, physicians uniformly conceptualised KOA as a relatively minor health problem, although they still recognised it as a painful condition that often limits patients' activities. Consequently, physicians did not regard KOA as a condition to be proactively and aggressively managed. The gap between physicians' and patients' conceptualisation of KOA and its treatment extended to the use of an app for self-management. While patients were supportive of the app, physicians were sceptical of its use and focused more on accountability and patient resources. CONCLUSIONS: The clear discord between physicians' mental models and patients' lived experience and perceived needs around KOA emphasised a gap in understanding and communication about treatment and management of KOA. As such, this preliminary and formative research will inform a codesign approach to develop an app that will act as a communications tool between patients and physicians, enabling patient-physician discussions regarding modifiable self-management options based on a patient's perspectives and needs.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".