A scoping review of patient engagement activities during COVID-19: More consultation, less partnership
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has had a devastating impact on healthcare systems and care delivery, changing the context for patient and family engagement activities. Given the critical contribution of such activities in achieving health system quality goals, we undertook to address the question: What is known about work that has been done on patient engagement activities during the pandemic? OBJECTIVE: To examine peer-reviewed and grey literature to identify the range of patient engagement activities, broadly defined (inclusive of engagement to support clinical care to partnerships in decision-making), occurring within health systems internationally during the first six months of the COVID-19 pandemic, as well as key barriers and facilitators for sustaining patient engagement activities during the pandemic. METHODS: The following databases were searched: Medline, Embase and LitCOVID; a search for grey literature focused on the websites of professional organizations. Articles were required to be specific to COVID-19, describe patient engagement activities, involve a healthcare organization and be published from March 2020 to September 2020. Data were extracted and managed using Microsoft Excel. A content analysis of findings was conducted. RESULTS: Twenty-nine articles were included. Few examples of more genuine partnership with patients were identified (such as co-design and organizational level decision making); most activities related to clinical level interactions (e.g. virtual consultations, remote appointments, family visits using technology and community outreach). Technology was leveraged in almost all reported studies to interact or connect with patients and families. Five main descriptive categories were identified: (1) Engagement through Virtual Care; (2) Engagement through Other Technology; (3) Engagement for Service Improvements/ Recommendations; (4) Factors Impacting Patient Engagement; and (5) Lessons Learned though Patient Engagement. CONCLUSIONS: Evidence of how healthcare systems and organizations stayed connected to patients and families during the pandemic was identified; the majority of activities involved direct care consultations via technology. Since this review was conducted over the first six months of the pandemic, more work is needed to unpack the spectrum of patient engagement activities, including how they may evolve over time and to explore the barriers and facilitators for sustaining activities during major disruptions like pandemics.
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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 | 0.030 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.022 | 0.030 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".