A co‐designed framework to support and sustain patient and family engagement in health‐care decision making
Bibliographic record
Abstract
BACKGROUND: Patient and family engagement in health care has emerged as a critical priority. Understanding engagement, from the perspective of the patient and family member, coupled with an awareness of how patient and family members are motivated to be involved, is an important component in increasing the effectiveness of patient engagement initiatives. The purpose of this research was to co-design a patient and family engagement framework. METHODS: Workshops were held to provide additional context to the findings from a survey. Participants were recruited using a convenience sampling strategy. Workshop data collected were analysed using a modified constant comparative technique. The core research team participated in a workshop to review the findings from multiple inputs to inform the final framework and participated in a face validity exercise to determine that the components of the framework measured what they were intended to measure. RESULTS: The framework is organized into three phases of engagement: why I got involved; why I continue to be involved; and what I need to strengthen my involvement. The final framework describes seven motivations and 24 statements, arranged by the three phases of engagement. CONCLUSION: The results of this research describe the motivations of patient and family members who are involved with health systems in various roles including as patient advisors. A deeper knowledge of patient and family motivations will not only create meaningful engagement opportunities but will also enable health organizations to gain from the voice and experience of these individuals, thereby enhancing the quality and sustainability of patient and family involvement.
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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 | 0.041 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".