The importance of instant impact: What matters to long‐term care staff and residents about taking part in research?
Bibliographic record
Abstract
There is an urgent need for research that can inform policy and practice in long-term care.1 However, issues such as high levels of staff turnover,2 misconceptions and negative attitudes about research,3, 4 and chronic staff shortages5 are obstacles to engaging long-term care staff and residents with research. The COVID-19 pandemic also exposed gaps in the research infrastructure within long-term care,1 highlighting the need for innovative approaches that could foster a collaborative research culture within the long-term care sector. Understanding what long-term care staff and residents think and feel about research is an important first step toward collaborative research engagement. We therefore drew on field notes that we took during our conversations between researchers, and 11 members of staff and seven residents from two long-term care homes in Toronto (Canada) and one in Manchester (UK), in May and July 2023, about their views and experiences of taking part in research. The staff roles represented included management/directorship, care assistance, nursing, programming/activity coordination, research leadership, and administration. In this article, we share some of the key insights gained from these conversations about how researchers could more effectively engage staff and residents in research.
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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 | 0.176 | 0.382 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.051 |
| Scholarly communication | 0.034 | 0.034 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.017 | 0.020 |
| 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".