Community engagement with immigrant communities involving health and wellness research: a systematic review protocol towards developing a taxonomy of community engagement definitions, frameworks, and methods
Bibliographic record
Abstract
INTRODUCTION: The importance of community engagement has been established globally in health and wellness research. A certain degree of ambiguity remains, however, regarding the meaning of community engagement, which term has been used for various purposes and implemented in various forms. In this study, we aimed to explore the different definitions of community engagement, discuss the various objectives that have been proposed and uncover the diverse ways this concept has been implemented among researchers working for the betterment of the health and wellness of immigrant communities in host countries. METHODS AND ANALYSIS: Taxonomy is a process for classifying complex and multifaceted matters using logical conceptual domains and dimensions for clearer way of contextualising. We will develop a taxonomy to organise the available literature on community engagement in immigrant health and wellness research in a way that captures user knowledge and understanding of its various meanings and processes. Specific methodological and analytical frameworks for systematic review and taxonomy development will guide each step. We will conduct a comprehensive systematic search in relevant databases, from inception to December 2019, using appropriate keywords followed by snowball search (single-citation tracking, reference lists). Papers will be included if they fall within predefined inclusion criteria (seen as most likely informative on elements pertaining to community engagement) and are written in English, regardless of design (conceptual, qualitative and quantitative). Two reviewers will independently employ two-stage screening (title-abstract screening followed by screening of the full text to determine inclusion). Finally, information that helps to develop taxonomy of the concept and practice of community engagement will be abstracted and used towards taxonomy development, where different levels of stakeholder research team members will be involved. ETHICS AND DISSEMINATION: Ethical approval is not required for this systematic review. We have opted for an integrated knowledge translation or a community-engaged knowledge mobilisation approach where we are engaged with community-based citizen researchers from the inception of our programme. We plan to disseminate the results of our review through meetings with key stakeholders, followed by journal publications and presentations at applicable platforms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.136 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.001 | 0.024 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".