Improving Data-Informed Care in New Brunswick Long-Term Care Homes: A Qualitative Study on an Educational Intervention for interRAI Coordinators
Bibliographic record
Abstract
Background/Objectives: InterRAI is a globally validated platform aimed at improving care for individuals with disabilities and complex medical needs, particularly in long-term care settings. This study explores the experiences of interRAI coordinators in New Brunswick, Canada, and their perceptions of an educational intervention designed to enhance their ability to effectively use interRAI data for quality care. Methods: The study recruited interRAI coordinators from 73 New Brunswick long-term care homes for an educational intervention. Nine coordinators participated in interviews about their experiences. A qualitative descriptive approach was used to analyze field notes and interview transcripts with thematic analysis. Results: Nine interviews and six sets of field notes were collected over one year, focusing on the roles of interRAI coordinators. Participants (all female, averaging 54 years old) expressed positive perceptions of the intervention, noting increased knowledge and collaboration. Key themes included the context of the interRAI coordinator role, the use of interRAI data for quality indicators, and recommendations for future educational initiatives. Conclusions: The findings emphasize the critical role of interRAI coordinators in improving quality care in long-term care settings through effective data use and collaboration. Participants reported that the educational intervention significantly improved their understanding and application of interRAI data. Recommendations for ongoing training and broader engagement stress the importance of continuous support to advance care quality in long-term care homes.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".