Challenges and Lessons Learned through Initiating Patient Engagement with Migrant People Living with HIV During the COVID-19 Outbreak (Preprint)
Bibliographic record
Abstract
UNSTRUCTURED Background Patient engagement (PE) refers to the meaningful and active involvement of patients and other stakeholders (i.e. family members) in the conduct of research and transfer of knowledge. PE is usually an immersive experience for both stakeholders and researchers, based on direct dialogue and equitable partnerships. However, in reaction to the COVID-19 pandemic, social distancing measures have been introduced globally. These measures, which may remain in effect for a long duration, or be re-introduced periodically, prevent in-person gathering, and thereby, foster dependence on technologies to remain connected remotely. This affects PE methods. Thus, an understanding of how remote work affects PE is necessary. Main Text In this narrative, we present the experience of a research team that began engaging an advisory committee of recent migrant people living with HIV in Montréal, Canada, amidst social distancing measures put in place due to COVID-19. We highlight three major challenges faced by our team of researchers and the advisory committee. These challenges include (1) ensuring access to technology for both patients and researchers; (2) managing disclosure and comfort with online tools; and (3) creating meaningful communication and peer-to-peer rapport. Subsequently, we list the main lessons we gained through responding to these challenges: (1) the importance of allowing time, dialogue, and reflection to enable adjustment to the new context we are working in; (2) the need to evolve our teamwork dynamics; and (3) implementing hands-on experiences for patients is essential to establishing feelings of meaningful engagement Conclusion PE is not an easy task and its implementation can become even more complex amidst social distancing measures and other disruptions caused by COVID-19 (i.e. fear of contracting COVID-19). However, if appropriate methods are taken up, PE can serve as an instrumental pillar for research activities that seek to create an impact in communities and populations.
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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 | 0.038 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.015 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".