A review of reviews on principles, strategies, outcomes and impacts of research partnerships approaches: a first step in synthesising the research partnership literature
Bibliographic record
Abstract
BACKGROUND: Conducting research in partnership with stakeholders (e.g. policy-makers, practitioners, organisations, patients) is a promising and popular approach to improving the implementation of research findings in policy and practice. This study aimed to identify the principles, strategies, outcomes and impacts reported in different types of reviews of research partnerships in order to obtain a better understanding of the scope of the research partnership literature. METHODS: This review of reviews is part of a Coordinated Multicenter Team approach to synthesise the research partnership literature with five conceptually linked literature reviews. The main research question was 'What principles, strategies, outcomes and impacts are reported in different types of research partnership approaches?'. We included articles describing a literature review of research partnerships using a systematic search strategy. We used an adapted version of the Revised Assessment of Multiple Systematic Reviews tool to assess quality. Nine electronic databases were searched from inception to April 2018. Principles, strategies, outcomes and impacts were extracted from the included reviews and analysed using direct content analysis. RESULTS: We included 86 reviews using terms describing several research partnership approaches (e.g. community-based participatory research, participatory research, integrated knowledge translation). After the analyses, we synthesised 17 overarching principles and 11 overarching strategies and grouped them into one of the following subcategories: relationship between partners; co-production of knowledge; meaningful stakeholder engagement; capacity-building, support and resources; communication process; and ethical issues related to the collaborative research activities. Similarly, we synthesised 20 overarching outcomes and impacts on researchers, stakeholders, the community or society, and the research process. CONCLUSIONS: This review of reviews is the first that presents overarching principles, strategies, outcomes and impacts of research partnerships. This review is unique in scope as we synthesised literature across multiple research areas, involving different stakeholder groups. Our findings can be used as a first step to guide the initiation and maintenance of research partnerships and to create a classification system of the key domains of research partnerships, which may improve reporting consistency in the research partnership literature. TRIAL REGISTRATION: This study is registered via Open Science Framework: https://doi.org/10.17605/OSF.IO/GVR7Y.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
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 | 0.090 | 0.308 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.036 | 0.042 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".