Insights into the impact and use of research results in a residential long-term care facility: a case study
Bibliographic record
Abstract
BACKGROUND: Engaging end-users of research in the process of disseminating findings may increase the relevance of findings and their impact for users. We report findings from a case study that explored how involvement with the Translating Research in Elder Care (TREC) study influenced management and staff at one of 36 TREC facilities. We conducted the study at 'Restwood' (pseudonym) nursing home because the Director of Care engaged actively in the study and TREC data showed that this site differed on some areas from other nursing homes in the province. The aims of the case study were two-fold: to gain a better understanding of how frontline staff engage with the research process, and to gain a better understanding of how to share more detailed research results with management. METHODS: We developed an Expanded Feedback Report for use during this study. In it, we presented survey results that compared Restwood to the best performing site on all variables and participating sites in the province. Data were collected regarding the Expanded Feedback Report through interviews with management. Data from staff were collected through interviews and observation. We used content analysis to derive themes to describe key aspects related to the study aims. RESULTS: We observed the importance of understanding organizational routines and the impact of key events in the facility's environment. We gleaned additional information that validated findings from prior feedback mechanisms within TREC. Another predominant theme was the sense that the opportunity to engage in a research process was reaffirming for staff (particularly healthcare aides)-what they did and said mattered, and TREC provided a means of having one's voice heard. We gained valuable insight from the Director of Care about how to structure and format more detailed findings to assist with interpretation and use of results. CONCLUSIONS: Four themes emerged regarding staff engagement with the research process: sharing feedback reports from the TREC study; the meaning of TREC to staff; understanding organizational context; and using the study feedback for improvement at Restwood. This study has lessons for researchers on how to share research results with study participants, including management.
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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 | 0.053 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.024 | 0.015 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".