Research impact of systems-level long-term care research: a multiple case study
Bibliographic record
Abstract
BACKGROUND: Traditional reporting of research outcomes and impacts, which tends to focus on research product publications and grant success, does not capture the value, some contributions, or the complexity of research projects. The purpose of this study was to understand the contributions of five systems-level research projects as they were unfolding at the Bruyère Centre for Learning, Research and Innovation (CLRI) in long-term care (LTC) in Ottawa, Ontario, Canada. The research questions were, (1) How are partnerships with research end-users (policymakers, administrators and other public/private organisations) characterised? (2) How have interactions with the CLRI Management Committee and Steering Committee influenced the development of research products? (3) In what way have other activities, processes, unlinked actors or organisations been influenced by the research project activities? METHODS: The study was guided by Kok and Schuit's concept of research impacts, using a multiple case study design. Data were collected through focus groups and interviews with research teams, a management and a steering committee, research user partners, and unlinked actors. Documents were collected and analysed for contextual background. RESULTS: Cross-case analysis revealed four major themes: (1) Benefits and Perceived Tensions: Working with Partners; (2) Speaking with the LTC Community: Interactions with the CLRI Steering Committee; (3) The Knowledge Broker: Interactions with the Management Committee; and (4) All Forms of Research Contributions. CONCLUSIONS: Most contributions were focused on interactions with networks and stimulating important conversations in the province about LTC issues. These contributions were well-supported by the Steering and Management Committees' research-to-action platform, which can be seen as a type of knowledge brokering model. It was also clear that researcher-user partnerships were beneficial and important.
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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 | 0.050 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| 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, 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".