The broad range of self-management strategies that people with rheumatic and musculoskeletal conditions apply: an online survey using a citizen science approach
Bibliographic record
Abstract
Rheumatic and musculoskeletal diseases (RMDs) cause several restrictions in daily living. Self-management is an important aspect of managing RMDs. However, little is known about the self-management strategies that are currently applied in daily life. This study aimed to identify the current self-management strategies that people with RMDs apply through a citizen science approach. An online survey was iteratively developed together with people with RMDs. The survey was distributed among people with all types of RMDs. Survey responses were collected within Qualtrics, and once anonymized, analysed using Atlas.ti. General self-management strategies and motivations to start performing a strategy were deductively coded by two reviewers, after consultations with patient partners. 250 complete surveys were collected. 91.2% of the respondents were female. 1305 self-management strategies were mentioned, and 669 elaborations were given. Most participants applied self-management strategies within the 'physical activity' category in their daily lives (e.g., walking, biking). Motivations to start performing a certain self-management strategy mostly originated from the bodily functioning dimension (e.g., reducing pain). 1275 facilitators to start a self-management strategy were mentioned, which were mostly related to the 'support' category. Barriers (N = 480) were most frequent in the 'condition-related' category. Self-management is an important aspect of managing a person's condition in daily life. People choose one or several strategies based on the challenge they are facing, depending on their feasibility and preferences in line with their personal context. The comprehensive overview of strategies informs both patients and healthcare professionals to support a personalized self-management journey.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".