Bridging the gap between science and care: a qualitative exploration of the role of the Scientific Linking Pin researcher working in research and practice partnerships
Bibliographic record
Abstract
Context: The Living Lab in Ageing and Long-Term Care (Netherlands) and Nurturing Innovation in Care Homes Excellence in Leeds (NICHE-Leeds; UK) are partnerships between science and care. The Scientific Linking Pin (SLP), a senior researcher employed by a university, works one day per week in a LTC organization, and has a pivotal role in the partnership. Objective: To explore the nature of the SLP role Methods: A qualitative approach was used. Fifteen individuals with at least one year’s experience as a SLP in the Living Lab or NICHE-Leeds participated in a semi-structured interview. Data were thematically analyzed. Findings: Participants described how the SLP role gave them insight into what matters to care organizations, and how it enabled them to impact LTC practice. Participants experienced the role to be multifaceted. Goals and activities performed by SLPs included developing relationships, raising awareness of the practice-science partnership, identifying (research) priorities and generating research questions, building committees, brokering knowledge, developing research studies, generating academic output, building links and connections, and assisting with internal projects. Challenges faced were mistrust by care staff and poor engagement, working with staff from different professional backgrounds, research not being a priority, multiple and rapidly changing priorities, and differences in expectations. SLPs addressed these challenges through relationship building, creating a ‘safe’ space for care staff, building engagement, and expectation management. Implications: Partnership working in the care sector is gaining international recognition and adoption, and therefore it is useful to capture and share learning about successful implementation of our approach.
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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 | 0.054 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.022 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.004 | 0.007 |
| 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".