Understanding the motivations of patients: A co‐designed project to understand the factors behind patient engagement
Bibliographic record
Abstract
BACKGROUND: Large-scale transformation depends on effective engagement of diverse stakeholders. With the evolution of the role of the 'patient partner' in health-care decision making, understanding the motivations of these individuals is essential to the success of engagement initiatives. This study reports on motivational factors associated with patient engagement in health care. METHODS: Patient co-investigators and a researcher co-designed and conducted this study. A survey was administered to patients and family members. Key informant interviews and previous research informed the development of the survey tool. The survey data were analysed using exploratory factor analysis to identify the underlying dimensions in the data. Cronbach's alpha was used to determine reliability. RESULTS: A total of 1449 individuals participated in the survey. Of these, 543 completed and 427 partially completed the survey (67% complete rate). The mean age of the respondents was 54 years. The majority of participants were female, well-educated, retired, married and lived in an urban centre. Seven motivational factors explained 65% of the total variance. Analysis of internal consistency revealed acceptable reliability for all items. The seven motivations were as follows: Self-fulfillment, Improving Healthcare, Compensation, Influence, Learning New Things, Conditional and Perks. CONCLUSION: The results of this research describe a sample of patient and family members currently engaged with health systems. We identified seven motivational factors underlying their engagement. A deeper knowledge of volunteer motivations will not only create meaningful engagement opportunities for patients, but also enable health organizations to gain from the experience of these individuals, thereby enhancing quality and sustainability of patient engagement programmes.
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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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".