Unregulated provider perceptions of audit and feedback reports in long-term care: cross-sectional survey findings from a quality improvement intervention
Bibliographic record
Abstract
BACKGROUND: Audit with feedback is a moderately effective approach for improving professional practice in other health care settings. Although unregulated caregivers give the majority of direct care in long-term care settings, little is known about how they understand and perceive feedback reports because unregulated providers have not been directly targeted to receive audit with feedback in quality improvement interventions in long-term care. The purpose of this paper is to describe unregulated care providers' perceptions of usefulness of a feedback report in four Canadian long-term care facilities. METHODS: We delivered monthly feedback reports to unregulated care providers for 13 months in 2009-2010. The feedback reports described a unit's performance in relation to falls, depression, and pain as compared to eight other units in the study. Follow-up surveys captured participant perceptions of the feedback report. We conducted descriptive analyses of the variables related to participant perceptions and multivariable logistic regression to assess the association between perceived usefulness of the feedback report and a set of independent variables. RESULTS: The vast majority (80%) of unregulated care providers (n = 171) who responded said they understood the reports. Those who discussed the report with others and were interested in other forms of data were more likely to find the feedback report useful for making changes in resident care. CONCLUSIONS: This work suggests that unregulated care providers can understand and feel positively about using audit with feedback reports to make changes to resident care. Further research should explore ways to promote fuller engagement of unregulated care providers in decision-making to improve quality of care in long-term care settings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".