Assessing the implementation of nurse practitioner-led huddles in long-term care using the Consolidated Framework for Implementation Research (CFIR)
Bibliographic record
Abstract
Abstract Background The COVID-19 pandemic created major challenges in long-term care (LTC) homes across Canada and globally. A nurse practitioner-led interdisciplinary huddle intervention was developed to support staff wellbeing in two LTC homes in Ontario, Canada. The objective of this study was to identify the constructs strongly influencing the process of implementation of huddles across both sites, capturing the overall barriers and facilitators and the intervention’s intrinsic properties. Methods Nineteen participants were interviewed about their experiences, pre-, post-, and during huddle implementation. The Consolidated Framework for Implementation Research (CFIR) was used to guide data collection and analysis. CFIR rating rules and a cross-comparison analysis was used to identify differentiating factors between sites. A novel extension to the CFIR analysis process was designed to assess commonly influential factors across both sites. Results Nineteen of twenty selected CFIR constructs were coded in interviews from both sites. Five constructs were determined to be strongly influential across both implementation sites and a detailed description is provided: evidence strength and quality; needs and resources of those served by the organization; leadership engagement; relative priority; and champions. A summary of ratings and an illustrative quote are provided for each construct. Conclusion Successful huddles require long-term care leaders to consider their involvement, the inclusion all team members to help build relationships and foster cohesion, and the integration of nurse practitioners as full-time staff members within LTC homes to support staff and facilitate initiatives for wellbeing. This research provides an example of a novel approach using the CFIR methodology, extending its use to identify significant factors for implementation when it is not possible to compare differences in success.
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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 | 0.234 | 0.235 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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 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".