Multi-stakeholder aged care research networks: a scoping review - Poster
Bibliographic record
Abstract
Background: Australia is undergoing major aged care reform to improve the quality and safety of aged care. A key enabler of quality improvement is strengthening evidence-based practice through creating a research translation ecosystem. Collaborative multi-stakeholder research networks offer a potential solution by bringing together all stakeholders to identify evidence to practice gaps, codesign research and translate knowledge into practice. This scoping review aimed to identify the key factors required to create sustainable and impactful multi-stakeholder aged care research networks.Methods: A literature search was completed in MEDLINE, EBSCO CINAHL+, Scopus, Emcare and Ageline to identify existing multi-stakeholder aged care research networks. A grey literature search was conducted using Google, Google Scholar, AAG, APO and a manual search of targeted websites.Results: The scoping review identified 29 papers/web-based resources for inclusion, reporting on six multi-stakeholder research networks from the UK, Canada, Australia, the Netherlands and the US. Networks were hard to find due to a lack of consistent terminology and significant overlap with other concepts such as teaching nursing homes. Enabling factors of successful networks included flexibility in structure, good governance, leveraging pre-existing research relationships, consistent and open communication, capacity building for all stakeholders, time and resources for dissemination and focus on building long term partnerships independent of research projects.Conclusions/Implications: Collaborative multi-stakeholder research networks offer promise for improving research translation in aged care. For the field to advance, it requires internationally agreed terminology for network models, clear reporting and evaluation guidelines and dedicated infrastructure funding for research networks.
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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 | 0.126 | 0.241 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.033 | 0.032 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".