Institutionalizing evidence-based practice: an organizational case study using a model of strategic change
Bibliographic record
Abstract
BACKGROUND: There is a general expectation within healthcare that organizations should use evidence-based practice (EBP) as an approach to improving the quality of care. However, challenges exist regarding how to make EBP a reality, particularly at an organizational level and as a routine, sustained aspect of professional practice. METHODS: A mixed method explanatory case study was conducted to study context; i.e., in terms of the presence or absence of multiple, inter-related contextual elements and associated strategic approaches required for integrated, routine use of EBP ('institutionalization'). The Pettigrew et al. Content, Context, and Process model was used as the theoretical framework. Two sites in the US were purposively sampled to provide contrasting cases: i.e., a 'role model' site, widely recognized as demonstrating capacity to successfully implement and sustain EBP to a greater degree than others; and a 'beginner' site, self-perceived as early in the journey towards institutionalization. RESULTS: The two sites were clearly different in terms of their organizational context, level of EBP activity, and degree of institutionalization. For example, the role model site had a pervasive, integrated presence of EBP versus a sporadic, isolated presence in the beginner site. Within the inner context of the role model site, there was also a combination of the Pettigrew and colleagues' receptive elements that, together, appeared to enhance its ability to effectively implement EBP-related change at multiple levels. In contrast, the beginner site, which had been involved for a few years in EBP-related efforts, had primarily non-receptive conditions in several contextual elements and a fairly low overall level of EBP receptivity. The beginner site thus appeared, at the time of data collection, to lack an integrated context to either support or facilitate the institutionalization of EBP. CONCLUSION: Our findings provide evidence of some of the key contextual elements that may require attention if institutionalization of EBP is to be realized. They also suggest the need for an integrated set of receptive contextual elements to achieve EBP institutionalization; and they further support the importance of specific interactions among these elements, including ways in which leadership affects other contextual elements positively or negatively.
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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 | 0.021 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".