Planning for implementation success: insights from conducting an implementation needs assessment
Bibliographic record
Abstract
AIM: The aim of this paper is to provide insights into conducting an implementation needs assessment using a case example in a less-research-intensive setting. DESIGN AND METHODS: In the case example, an implementation needs assessment was conducted, including (1) an environmental scan of the organization's website and preliminary discussions with key informants to learn about the implementation context, and (2) a formal analysis of the evidence-practice gap (use of sedation interruptions) deploying a chart audit methodology using legal electronic reports. RESULTS: Our needs assessment was conducted over 5 months and demonstrated how environmental scans reveal valuable information that can inform the evidence-practice gap analysis. A well-designed gap analysis, using suitable indicators of best practice, can reveal compliance rates with local protocol recommendations, even with a small sample size. In our case, compliance with the prescribed practices for sedation interruptions ranged from 65% (n=53) to as high as 84% (n=69). CONCLUSIONS: Implementation needs assessments provide valuable information that can inform implementation planning. Such assessments should include an environmental scan to understand the local context and identify both current recommended best practices and local best practices for the intervention of interest. When addressing an evidence-practice gap, analyses should quantify the difference between local practice and desired best practice. IMPACT: The insights gained from the case example presented in this paper are likely transferrable to implementation research or studies conducted in similar, less-research-intensive settings. SPANISH ABSTRACT: http://links.lww.com/IJEBH/A257.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".