Nurturing Acceptance for Research in the Community: Conceptualising Engagement Towards Research Through Normalisation Process Theory
Bibliographic record
Abstract
BACKGROUND: Community-engaged research with immigrant and visible minority communities requires intentional strategies to foster acceptance, trust and sustained participation. Historically, research in marginalised communities has been extractive and externally driven, leading to mistrust and scepticism. OBJECTIVES: To address this, we applied Normalisation Process Theory (NPT) as a guiding framework to integrate research as a meaningful, community-driven practice rather than an extractive academic exercise. The objective of this paper is to describe how NPT can illuminate the social and relational processes involved in introducing, legitimising and maintaining collaborative research practices within a community. METHODS: Using NPT's four constructs-Coherence (establishing the 'why'), Cognitive Participation (generating the 'will'), Collective Action (carrying out the 'tasks') and Reflexive Monitoring (reflecting and adapting)-we structured a phased approach to community engagement. Our initiatives included community-focused outreach, community organisation for capacity-building and collaborative research activities, all designed to shift research from being externally imposed to community-engaged. A key challenge was achieving initial acceptance of research within the community. RESULTS: Through intentional outreach, inclusive recruitment and participatory knowledge production, we transitioned from establishing legitimacy to building long-term, community-driven partnerships. CONCLUSION: Our experience highlight the importance of embedding research within everyday community life, valuing local expertise and ensuring that knowledge production remains collaborative, accessible and action-oriented. This approach not only bridges the gap between academia and the community but also fosters equitable, enduring research relationships that lead to meaningful, sustainable impact. PATIENT OR PUBLIC CONTRIBUTION: While preparing this manuscript, we have partnered actively with community scholars and citizen researchers from the very beginning. We had regular interactions with them to get their valuable and insightful inputs in shaping our reflections. Their involvement as co-authors in this paper also provided a learning opportunity for them and facilitated them to gain insight into knowledge engagement. All authors support the greater community/citizen/public involvement in research in an equitable manner.
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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 | 0.101 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.009 | 0.125 |
| Scholarly communication | 0.026 | 0.028 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".