Optimizing a knowledge translation intervention: a qualitative formative study to capture knowledge translation needs in nursing homes
Bibliographic record
Abstract
BACKGROUND: Knowledge translation (KT) has emerged as an important consideration to reduce knowledge-to-practice gaps in healthcare settings. Research on KT approaches in nursing homes (NHs) is lacking. There is a need to understand the challenges faced in NHs and how these can be managed. This study is part of the larger IMPAKT (IMPlementation and Action for Knowledge Translation) study which addresses KT in NHs. The aim of the study presented here was to identify crucial staff and organizational needs in order to inform the development of a KT intervention in NHs. METHODS: A multimethod qualitative approach was applied. We invited practice development nurses (PDNs) to describe current practice, and to identify problems and needs concerning KT in NHs. We followed the recommendations of the development phase of the MRC framework for developing complex interventions. Data were collected through four focus groups and participatory observations in six NHs. Analysis was conducted according to structural thematic analysis based on a phenomenological hermeneutic method. RESULTS: We identified three themes that expressed the PDNs' perceived needs for successful KT implementation: (1) narrowing the PDN role, (2) developing an EBP culture and (3) establishing collaborative alliances. Nine subthemes derived from the PDNs' experiences and current practice, illustrating needs at individual, relational and organizational levels. CONCLUSIONS: Rigorous development of complex interventions may add relevance to the intervention, increase the likelihood of success and reduce research waste. Insight into the NH context and organization have helped us define problems and articulate needs that must be addressed when tailoring the IMPAKT intervention. TRIAL REGISTRATION: The IMPAKT trial was retrospectively registered in the ISRCTN Registry (Trial ID: 12,437,773) on March 19th, 2020.
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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 | 0.074 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| 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".