A qualitative study exploring how patient engagement activities were sustained or adapted in Canadian healthcare organizations during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic caused disruptions across healthcare systems globally exposing the precarious state of patient engagement across all levels of healthcare. While evidence is emerging to describe how engagement was affected across various settings, insights about how some organizations at the policy and practice level of healthcare were able to sustain or adapt patient engagement activities is lacking. OBJECTIVE: This paper addresses the following research question: "How were healthcare, government, and patient partner organizations able to sustain or adapt patient engagement activities during the COVID-19 pandemic?" METHODS: A qualitative descriptive study was conducted to understand how patient engagement activities were maintained or adapted in a variety of healthcare, government, and patient partner organizations in Canada throughout the pandemic. This analysis was part of a larger qualitative, multiple case study where one-to-one interviews were conducted with organizational leaders, managers and patient partners. RESULTS: The following themes were identified as key aspects of maintaining or adapting patient engagement activities: 1) having an embedded organizational culture of patient engagement; 2) adapting patient engagement activities to focus on COVID-19 response efforts; 3) having patient partners who exercised leadership and advocacy to support patient care and experiences during the pandemic; and 4) leveraging virtual technology as a communication tool to engage patient partners. CONCLUSION: This paper highlights important insights that may be useful to other health care organizations on how to sustain or adapt patient engagement activities during a healthcare crisis. Having patient engagement embedded within an organization's culture supported by, but not limited to, infrastructure, resources, investments in dedicated staff and patient partner leadership, and communication strategies and tools enabled continued patient engagement activities during the pandemic.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.034 | 0.017 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".