Implementing a new clinical service – what’s your elevator pitch?
Bibliographic record
Abstract
BACKGROUND: People with multiple sclerosis (PwMS) identify emotional well-being as a key unmet care need. Mindfulness-based interventions (MBI) can improve emotional well-being in PwMS; however, there is a lack of information on their implementation in routine care. Healthcare policy influencers may provide critical insight as to the implementation process. The aim of this study was to explore the needs and priorities of healthcare policy influencers for implementing MBIs for PwMS in Canada. METHODS: A qualitative descriptive approach was adopted using semi-structured interviews with an inductive thematic analysis. Healthcare policy influencers (e.g., senior clinical leaders, provisional health service commissioners, healthcare policymakers) in various settings across Ontario were recruited. RESULTS: Twelve individuals with an average age of 51.1 ± 8.9 years participated in the semi-structured interviews. Interviews ranged from 12 to 60 min. Four themes were identified in thematic analysis: (1) Need for evidence with a personal connection is foundational; (2) People Power: Need for Implementation champions; (3) Finding its place: Need for embedding interventions into existing systems; and (4) Sustainability: Need for focus on long-term impact. CONCLUSION: Our study provides novel insight into complex factors which affect implementation of new interventions, such as MBIs for PwMS, into the healthcare landscape in Ontario. Six key steps were identified for implementors to consider when seeking to implement a new intervention: (1) identify the problem and the need for intervention, (2) establish evidence highlighting evidence of effectiveness for an intervention, (3) build a team of implementation champions, (4) pilot the novel intervention to establish proof of concept, feasibility, and ecological integration within current landscape, (5) identify decision makers for intervention implementation, and (6) develop an 'elevator pitch' for decision makers. The implementation process is convoluted and can lack clarity. This is a major challenge for implementers. We have identified six key steps for implementers to consider, making this process more transparent and hopefully more successful. Future research should explore, test, and bridge the gaps in the implementation pathway we have identified, as this may be critical in closing the gaps that exist in our healthcare systems.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".