Conceptualizing success factors for patient engagement in patient medical homes: a cross-sectional survey
Bibliographic record
Abstract
Background: Patient engagement is a priority for health care quality improvement and health system design, but many organizations struggle to engage patients meaningfully. We describe patient engagement activities and success factors that influence organizational decision-making in Ontario’s patient medical homes. Methods: From March to May 2018, we conducted an online survey focused on practice-level patient engagement that targeted primary care organization leaders at all Ontario family health teams, community health centres, nurse practitioner–led clinics and Aboriginal Health Access Centres. We asked questions from the Measuring Organizational Readiness for Engagement (MORE) and Public and Patient Engagement Evaluation Tool (PPEET) questionnaires. We used factor and mediation analysis to identify organizational conditions and activities that are associated with the outcomes of patient engagement, affecting board decisions, program-level decisions and the formation of collaborative partnerships. Results: We achieved a 53% response rate (n = 149/283); after removing missing data, our final sample size was 141 respondents. Most respondents perceived that their organization’s patient engagement activities and resources were insufficient. Processes that had a direct effect on outcomes (β = 0.7, p < 0.0001) included planning, training and supporting employees; identifying, recruiting and supporting relevant patients; and using leaders. Structures — including an organizational mission and vision for patient engagement, and policies, procedures, job positions, training programs and organizational culture that reflect that mission — indirectly affected outcomes, mediated by the aforementioned processes (β = 0.7, p < 0.0001). Interpretation: Based on the perceptions of primary care leaders, organizational structures and processes are related to successful patient engagement. Organizations that seek to improve patient engagement should assess their commitment and follow-through with associated resources and activities.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".