HowNotToDoPatientEngagement: The Engaging with Purpose Patient Engagement Framework based on a Twitter Analysis of Community Perspectives on Patient Engagement
Bibliographic record
Abstract
Abstract Background: Evaluation of patient engagement practices are frequently researcher-driven, researcher-funded and asymmetric in power dynamics. Little to almost no literature exists that documents patient experiences in patient engagement that are organic and non-institutional. Understanding these perspectives can help us understand what matters to patients when they are engaged in research, why it matters to them, and can offer insights on how to improve the patient engagement process so that the needs and priorities of patients are met. Methods: Twitter is a microblogging platform that contains a vast amount of freely available, user generated, textual data containing personal reflections and opinions. We conducted a conventional and summative content analysis of textual data retrieved from a highly active conversation on Twitter around the hashtags #HowNotToDoPatientEngagement and #HowToDoPatientEngagement between February 2018 to June 2021. Results: A total of 276 tweets were retrieved from 178 separate contributors across seven geographical locations. Four stakeholder groups were identified. We generated 24 codes, seven subthemes and five overarching themes: respect, support, collaboration, inclusivity and impact. Four of these themes are closely aligned with the Strategy for Patient Oriented (SPOR) Patient Engagement framework. We identify impact as a separate and new theme. Interpretation: Based on our findings we offer the Engaging with Purpose Patient Engagement Framework that defines and describes respect, support, collaboration, inclusivity and impact as five key pillars of meaningful patient engagement.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".