Successful Strategies to Sustain Practice Changes in Healthcare
Bibliographic record
Abstract
Healthcare managers implement evidence-based practice to meet the growing needs of aging populations. However, many healthcare leaders fail to sustain newly implemented practices. Grounded in the promoting action on research implementation in health services conceptual framework, the purpose of this qualitative interpretative descriptive study was to explore strategies healthcare leaders use to sustain practice changes to meet increasing demands for quality care of the aging population. The participants included eight healthcare managers from Canada and the United States who led strategies to sustain practice change. The four themes that emerged using semistructured interviews and thematic analysis were staff buy-in, staff feedback, roles to support sustainment, and flexibility to change. A key recommendation is for healthcare managers to use organizational structures to engage staff routinely during sustainment. Implications for social change include the potential to improve the quality of care delivered to patients.
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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 | 0.054 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".