Evaluating Real-World Implementation of INFORM (Improving Nursing Home Care through Feedback on Performance Data): An Improvement Initiative in Canadian Nursing Homes
Bibliographic record
Abstract
BACKGROUND: INFORM (Improving Nursing Home Care through Feedback on Performance Data) was a research intervention that equipped nursing home managers with skills to conduct local improvement projects and supported them in improving performance through modifiable elements in their units. Prior reports have found positive and sustained outcomes from INFORM intervention. In this article, the authors report findings from a formative service evaluation of INFORM as modified for implementation in real-world settings. METHODS: INFORM was transformed for real-world implementation with an initial cohort of 26 nursing homes in British Columbia, Canada (INFORM BC). Three stakeholder groups were involved: nursing home teams, an academic team that modified INFORM for implementation, and a BC team that implemented INFORM and coached participating nursing home teams in applying it locally. Service evaluation was conducted drawing on participants from all three stakeholder groups, using convenience sampling, with numbers varying by data source. Using a mixed methods design, outcome data included qualitative and quantitative assessment of surveys, discussions, observations, and a review of documents and resources. RESULTS: The majority of nursing home teams reported positive outcomes relative to the usefulness and relevance of the initiative for local needs despite a number of operational challenges during implementation. A key factor in their success was combining targeted external support with the opportunity to set goals and measure success locally. Challenges included a lack of time at the nursing home level, COVID-19-related disruptions, and issues with role clarity and alignment of expectations among the academic and BC teams. CONCLUSION: INFORM BC advanced the processes of change planning and transferable learning among nursing home managers and their local teams. Success was facilitated externally but defined and achieved locally. Future iterations should probe outcome sustainability and how nursing home teams adapt the INFORM approach in practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".