Collaborations between health services and educational institutions to develop research capacity in health services and health service staff: a systematic scoping review
Bibliographic record
Abstract
BACKGROUND: Participation of health service staff in research improves health outcomes and adherence to clinical guidelines. To increase research participation, many health services seek to build research capacity which adds to the development of individual and organisational skills and abilities in order to conduct health research. Numerous approaches to research capacity building have been trialed with inter- and intra-institutional, or university-health service collaborative approaches being frequently described strategies. University-health service research collaborations have potential for high impact and mutual benefit, by harnessing respective strengths across both organisations. However, the range and scope of research capacity building approaches, including their relative value and success have not been consolidated. The aim of this review was to examine and describe the collaborative strategies employed by health services in conjunction with educational partners to enhance the research capability of health service staff. METHODS: The scoping review framework by Arksey and O'Malley was used to inform the review method. A systematic search was conducted of four major databases: Medline, CINAHL, Embase, and Cochrane, focusing on publications after 1995. Inclusion and exclusion criteria were established through iterative team discussions. The two-stage screening process and data extraction was managed in Covidence. Collaboration, Research Capacity, Health Services, and Health workforce were the primary concepts, contexts and populations guiding the search. RESULTS: Of the 1462 studies identified, 61 were selected for the review. These studies reported on partnerships between universities and health services with a specific focus on building research capacity of health service staff. Studies predominantly hailed from Australia, USA, UK, and Canada. Collaboration approaches varied and leveraged different activities to build research capacity included training, mentoring, shared funding, and networking. Training partnerships emerging as the most prevalent. Findings emphasised the importance of localisation in approaches, with some studies indicating the intrinsic value of such collaborations for both partners involved. Despite the emphasis on individual interventions like training and mentoring, team-level interventions were notably scarce. CONCLUSION: This review highlights the diverse range of approaches in research capacity building collaborations between health services and educational partners. It advocates for a shared understanding of goals, highlighting the critical nature of relationship-building and the pivotal role of sustainable infrastructure in long-term collaboration success. Future directions should consider the tangible impacts of these models on clinical outcomes.
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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 | 0.078 | 0.225 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.034 | 0.035 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".