The SITS framework: sustaining innovations in tertiary settings
Bibliographic record
Abstract
Background To date, little attention has focused on what the determinants are and how evidence-based practices (EBPs) are sustained in tertiary settings (i.e., acute care hospitals). Current literature reveals several frameworks designed for implementation of EBPs (0–2 years), yet fewer exist for the sustainment of EBPs (>2 years) in clinical practice. Frameworks containing both phases generally list few determinants for the sustained use phase, but rather state ongoing monitoring or evaluation is necessary. Notably, a recent review identified six constructs and related strategies that facilitate sustainment, however, the pairing of determinants and how best to sustain EBPs in tertiary settings over time remains unclear. The aim of this paper is to present an evidence-informed framework, which incorporates constructs, determinants, and knowledge translation interventions (KTIs) to guide implementation practitioners and researchers in the ongoing use of EBPs over time. Methods We combined the results of a systematic review and theory analysis of known sustainability frameworks/models/theories (F/M/Ts) with those from a case study using mixed methods that examined the ongoing use of an organization-wide pain EBP in a tertiary care center (hospital) in Canada. Data sources included peer-reviewed sustainability frameworks ( n = 8) related to acute care, semi-structured interviews with nurses at the department ( n = 3) and unit ( n = 16) level, chart audits ( n = 200), and document review ( n = 29). We then compared unique framework components to the evolving literature and present main observations. Results We present the Sustaining Innovations in Tertiary Settings (SITS) framework which consists of 7 unique constructs, 49 determinants, and 29 related KTIs that influence the sustainability of EBPs in tertiary settings. Three determinants and 8 KTIs had a continuous influence during implementation and sustained use phases. Attention to the level of application and changing conditions over time affecting determinants is required for sustainment. Use of a participatory approach to engage users in designing remedial plans and linking KTIs to target behaviors that incrementally address low adherence rates promotes sustainability. Conclusions The SITS framework provides a novel resource to support future practice and research aimed at sustaining EBPs in tertiary settings and improving patient outcomes. Findings confirm the concept of sustainability is a “dynamic ongoing phase”.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".