Understanding patient engagement in health system decision-making: a co-designed scoping review
Bibliographic record
Abstract
BACKGROUND: With healthcare striving to shift to a more person-centered delivery model, patient and family involvement must have a bigger role in shaping this. While many initiatives involving patients and family members focus on self-care, a broader understanding of patient participation is necessary. Ensuring a viable and sustainable critical number of qualified patients and family members to support this shift will be of utmost importance. The purpose of this study was to understand how health systems are intentionally investing in the training and skill development of patients and family members. METHODS: Patient co-investigators and researchers conducted a scoping review of the existing literature on methods adopted by healthcare systems to build the skills and capacity of patients to participate in healthcare decision-making using a recognized methodological framework. Six electronic databases were searched to identify studies. Two independent reviewers screened titles and abstracts and full-text papers for inclusion. The research team independently extracted data. Any disagreements were resolved by achieving consensus through discussion. Quantitative and qualitative content synthesis, as well as a quality assessment, was conducted. RESULTS: After eliminating duplicates, the search resulted in 9428 abstracts. Four hundred fifty-eight articles were reviewed and 15 articles were included. Four themes emerged: forums (33%), patient instructors (20%), workshops (33%), and co-design (13%). Four of the identified studies measured the impact and overall effectiveness of the respective programs. Examples of how patient and family members were supported (invested in) included advocacy training to support future involvement in engagement activities, a training program to conduct patient-led research, involvement in an immersive experience-based co-design initiative, and involvement in training pharmacy students. Overall, these studies found positive outcomes when patients and family members were recipients of these opportunities. CONCLUSIONS: The results of this scoping review demonstrate that an evidence base around programs to advance patient engagement is largely absent. An opportunity exists for further research to identify strategies and measures to support patient engagement in healthcare decision-making.
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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 | 0.114 | 0.280 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.045 | 0.036 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.007 | 0.004 |
| 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".