Contextual factors and mechanisms that influence sustainability: a realist evaluation of two scaled, multi-component interventions
Bibliographic record
Abstract
BACKGROUND: (SCNs) to develop and implement evidence-informed, clinician-led and team-delivered health system improvement in Alberta, Canada. SCNs have had several provincial successes in improving health outcomes. Little research has been done on the sustainability of these evidence-based implementation efforts. METHODS: We conducted a qualitative realist evaluation using a case study approach to identify and explain the contextual factors and mechanisms perceived to influence the sustainability of two provincial SCN evidence-based interventions, a delirium intervention for Critical Care and an Appropriate Use of Antipsychotics (AUA) intervention for Senior's Health. The context (C) + mechanism (M) = outcome (O) configurations (CMOcs) heuristic guided our research. RESULTS: We conducted thirty realist interviews in two cases and found four important strategies that facilitated sustainability: Learning collaboratives, audit & feedback, the informal leadership role, and patient stories. These strategies triggered certain mechanisms such as sense-making, understanding value and impact of the intervention, empowerment, and motivation that increased the likelihood of sustainability. For example, informal leaders were often hands-on and influential to front-line staff. Learning collaboratives broke down professional and organizational silos and encouraged collective sharing and learning, motivating participants to continue with the intervention. Continual audit-feedback interventions motivated participants to want to perform and improve on a long-term basis, increasing the likelihood of sustainability of the two multi-component interventions. Patient stories demonstrated the interventions' impact on patient outcomes, motivating staff to want to continue doing the intervention, and increasing the likelihood of its sustainability. CONCLUSIONS: This research contributes to the field of implementation science, providing evidence on key strategies for sustainability and the underlying causal mechanisms of these strategies that increases the likelihood of sustainability. Identifying causal mechanisms provides evidence on the processes by which implementation strategies operate and lead to sustainability. Future work is needed to evaluate the impact of informal leadership, learning collaboratives, audit-feedback, and patient stories as strategies for sustainability, to generate better guidance on planning sustainable improvements with long term impact.
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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 | 0.029 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".