Collaboration between biomedical research and community-based primary health care actors in chronic disease management: a scoping review
Bibliographic record
Abstract
Abstract Background Collaboration between biomedical research and community-based primary health care actors is essential to translate evidence into clinical practice. However, little is known about the characteristics and impacts of implementing collaborative models. Thus, we sought to identify and describe collaboration models that bridge biomedical research and community-based primary health care in chronic disease management. Methods We conducted a scoping review using Medline, Embase, Web of Science, and Cochrane Library from inception to November 2020, to identify studies describing or evaluating collaboration models. We also searched grey literature, screened reference lists, and contacted experts to retrieve further relevant references. The list of studies was then refined using more specific inclusion and exclusion criteria. Two reviewers independently selected studies and extracted relevant data (characteristics of studies, participants, collaborations, and outcomes). No bias assessment was performed. A panel of experts in the field was consulted to interpret the data. Results were presented with descriptive statistics and narrative synthesis. Results Thirteen studies presenting 20 unique collaboration models were included. These studies were conducted in North America (n = 7), Europe (n = 5) and Asia (n = 1). Collaborations were implemented between 1967 and 2014. They involved a variety of profiles including biomedical researchers (n = 20); community-based primary health care actors (n = 20); clinical researchers (n = 15); medical specialists (n = 6); and patients, citizens, or users (n = 5). The main clinical focus was cardiovascular disease (n = 8). Almost half of the collaborations operated at an international level (n = 9) and the majority adopted either a network (n = 7) or hierarchical structure (n = 6). We identified significant implementation barriers (lack of knowledge, financial support, and robust management structure) and collaboration facilitators (partnership, cooperation, multidisciplinary research teams). Out of the 20 included collaboration models, seven reported measurable impact. Conclusion We identified a large variety of collaboration models representing several clinical and research profiles and fields of expertise. As they are all based in high-income countries, further research should aim to identify collaborations in low-income countries, to determine which models and/or characteristics, could better translate evidence into clinical practice in these contexts.
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 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 | 0.069 | 0.244 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.032 | 0.036 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".