Making Research Results Relevant and Useable: Presenting Complex Organizational Context Data to Nonresearch Stakeholders in the Nursing Home Setting
Bibliographic record
Abstract
BACKGROUND: The success of evidence-based practice depends on clearly and effectively communicating often complex data to stakeholders. In our program of research, Translating Research in Elder Care (TREC), we focus on improving the quality and safety of care delivered to nursing home residents in western Canada. More specifically, we investigate associations among organizational context, the use of best practices and resident outcomes. Our data are complex and we have been challenged with presenting these data in a way that is not only intuitive, but also useful for our stakeholders. AIM: To illustrate a technique of organizing and presenting complex data to nonresearch stakeholders. METHODS: Using observational data previously collected within the TREC study, we used k-means cluster analysis to categorize nursing home resident care units or facilities within our sample into two distinct groups-those with more favorable contexts (work environment) and those with less favorable contexts. We then produced scatter plots to illustrate group differences between context and various quality indicators among resident care units or facilities. RESULTS: Care aides working on units with more favorable context reported higher use of best practices. When aggregated at the nursing home facility level, facilities with low rates of both urinary tract infections and indwelling catheter use are higher in organizational context. When feeding back these results to stakeholders, we identify their units so that they are able to visually assess their units, both relative to each other and relative to all other units and facilities both within and among provinces. LINKING EVIDENCE TO ACTION: Although we have not formally evaluated this method, we have used it extensively as part of the feedback we provide to stakeholders. As we are examining modifiable aspects of context, the stakeholder can then identify areas for improvement and thus implement a focused plan.
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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 | 0.257 | 0.430 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.035 | 0.030 |
| Open science | 0.006 | 0.027 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".