Data for improvement and clinical excellence: protocol for an audit with feedback intervention in home care and supportive living
Bibliographic record
Abstract
BACKGROUND: Although considerable evidence exists about the effectiveness of audit coupled with feedback, very few audit-with-feedback interventions have been done in either home care or supportive living settings to date. With little history of audit and feedback in home care or supportive living there is potential for greater effects, at least initially. This study extends the work of an earlier study designed to assess the effects of an audit-with-feedback intervention. It will be delivered quarterly over a one-year period in seven home care offices and 11 supportive living sites. The research questions are the same as in the first study but in a different environment. They are as follows: 1. What effects do feedback reports have on processes and outcomes over time? 2. How do different provider groups in home care and supportive living sites respond to feedback reports based on quality indicator data? METHODS: The research team conducting this study includes researchers and decision makers in continuing care in the province of Alberta, Canada. The intervention consists of quarterly feedback reports in 19 home care offices and supportive living sites across Alberta. Data for the feedback reports are based on the Resident Assessment Instrument Home Care tool, a standardized instrument mandated for use in home care and supportive living environments throughout Alberta. The feedback reports consist of one page, printed front and back, presenting both graphic and textual information. Reports are delivered to all employees working in each site. The primary evaluation uses a controlled interrupted time-series design, both adjusted and unadjusted for covariates. The concurrent process evaluation includes observation, focus groups, and self-reports to assess uptake of the feedback reports. The project described in this protocol follows a similar intervention conducted in our previous study, Data for Improvement and Clinical Excellence--Long-Term Care. We will offer dissemination strategies and spread of the feedback report approach in several ways suited to various audiences and stakeholders throughout Alberta. SIGNIFICANCE: This study will generate knowledge about the effects of an audit with feedback intervention in home care and supportive living settings. Our dissemination activities will focus on supporting sites to continue to use the Resident Assessment Instrument data in their quality improvement activities.
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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 | 0.114 | 0.123 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.081 | 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".