Engaging Consumers with Musculoskeletal Conditions in Health Research: A User-Centred Perspective
Bibliographic record
Abstract
Consumers are frequently involved in different kinds of health research, such as clinical trials, focus groups, and surveys. As pointed out by different studies, recruiting and involving consumers to participate in academic research can be challenging. While different research and guidelines are provided to instruct researchers to recruit participants ethically, they seldom consider the needs and expectations of consumers. In this research, we interviewed 23 consumers with musculoskeletal conditions in Australia, to understand their needs and motivations for participating in research from a user-centred perspective. Based on these data, we systematically summarise consumers' feedback into four main themes: (1) Research as Learning Opportunity; (2) The Important Role of Communities and Health Professionals; (3) Research Transparency and Updates; and (4) Special Needs for People with MSK Conditions. As a result, a few recommendations are proposed and researchers should further consider these when designing consumer-based studies. Ultimately, with a better understanding of consumers, we hope that our research can enhance consumer engagement and improve their participation in health research.
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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 | 0.104 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".