Improving quality of care through routine, successful implementation of evidence-based practice at the bedside: an organizational case study protocol using the Pettigrew and Whipp model of strategic change
Bibliographic record
Abstract
BACKGROUND: Evidence-based practice (EBP) is an expected approach to improving the quality of patient care and service delivery in health care systems internationally that is yet to be realized. Given the current evidence-practice gap, numerous authors describe barriers to achieving EBP. One recurrently identified barrier is the setting or context of practice, which is likewise cited as a potential part of the solution to the gap. The purpose of this study is to identify key contextual elements and related strategic processes in organizations that find and use evidence at multiple levels, in an ongoing, integrated fashion, in contrast to those that do not. METHODS: The core theoretical framework for this multi-method explanatory case study is Pettigrew and Whipp's Content, Context, and Process model of strategic change. This framework focuses data collection on three entities: the Why of strategic change, the What of strategic change, and the How of strategic change, in this case related to implementation and normalization of EBP. The data collection plan, designed to capture relevant organizational context and related outcomes, focuses on eight interrelated factors said to characterize a receptive context. Selective, purposive sampling will provide contrasting results between two cases (departments of nursing) and three embedded units in each. Data collection methods will include quantitative tools (e.g., regarding culture) and qualitative approaches including focus groups, interviews, and documents review (e.g., regarding integration and "success") relevant to the EBP initiative. DISCUSSION: This study should provide information regarding contextual elements and related strategic processes key to successful implementation and sustainability of EBP, specifically in terms of a pervasive pattern in an acute care hospital-based health care setting. Additionally, this study will identify key contextual elements that differentiate successful implementation and sustainability of EBP efforts, both within varying levels of a hospital-based clinical setting and across similar hospital settings interested in EBP.
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 | 0.114 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".