Improving Nursing Home Care through Feedback On PerfoRMance Data (INFORM): Protocol for a cluster-randomized trial
Bibliographic record
Abstract
BACKGROUND: Audit and feedback is effective in improving the quality of care. However, methods and results of international studies are heterogeneous, and studies have been criticized for a lack of systematic use of theory. In TREC (Translating Research in Elder Care), a longitudinal health services research program, we collect comprehensive data from care providers and residents in Canadian nursing homes to improve quality of care and life of residents, and quality of worklife of caregivers. The study aims are to a) systematically feed back TREC research data to nursing home care units, and b) compare the effectiveness of three different theory-based feedback strategies in improving performance within care units. METHODS: INFORM (Improving Nursing Home Care through Feedback On PerfoRMance Data) is a 3.5-year pragmatic, three-arm, parallel, cluster-randomized trial. We will randomize 67 Western Canadian nursing homes with 203 care units to the three study arms, a standard feedback strategy and two assisted and goal-directed feedback strategies. Interventions will target care unit managerial teams. They are based on theory and evidence related to audit and feedback, goal setting, complex adaptive systems, and empirical work on feeding back research results. The primary outcome is the increased number of formal interactions (e.g., resident rounds or family conferences) involving care aides - non-registered caregivers providing up to 80% of direct care. Secondary outcomes are a) other modifiable features of care unit context (improved feedback, social capital, slack time) b) care aides' quality of worklife (improved psychological empowerment, job satisfaction), c) more use of best practices, and d) resident outcomes based on the Resident Assessment Instrument - Minimum Data Set 2.0. Outcomes will be assessed at baseline, immediately after the 12-month intervention period, and 18 months post intervention. DISCUSSION: INFORM is the first study to systematically assess the effectiveness of different strategies to feed back research data to nursing home care units in order to improve their performance. Results of this study will enable development of a practical, sustainable, effective, and cost-effective feedback strategy for routine use by managers, policy makers and researchers. The results may also be generalizable to care settings other than nursing homes. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02695836 . Date of registration: 24 February 2016.
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 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 | 0.063 | 0.060 |
| Meta-epidemiology (narrow) | 0.010 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.008 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.108 | 0.022 |
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".