The unpredictable journeys of spreading, sustaining and scaling healthcare innovations: a scoping review
Bibliographic record
Abstract
Innovation has the potential to improve the quality of care and health service delivery, but maximising the reach and impact of innovation to achieve large-scale health system transformation remains understudied. Interest is growing in three processes of the innovation journey within health systems, namely the spread, sustainability and scale-up (3S) of innovation. Recent reviews examine what we know about these processes. However, there is little research on how to support and operationalise the 3S. This study aims to improve our understanding of the 3S of healthcare innovations. We focus specifically on the definitions of the 3S, the mechanisms that underpin them, and the conditions that either enable or limit their potential. We conducted a scoping review, systematically investigating six bibliographic databases to search, screen and select relevant literature on the 3S of healthcare innovations. We screened 641 papers, then completed a full-text review of 112 identified as relevant based on title and abstract. A total of 24 papers were retained for analysis. Data were extracted and synthesised through descriptive and inductive thematic analysis. From this, we develop a framework of actionable guidance for health system actors aiming to leverage the 3S of innovation across five key areas of focus, as follows: (1) focus on the why, (2) focus on perceived-value and feasibility, (3) focus on what people do, rather than what they should be doing, (4) focus on creating a dialogue between policy and delivery, and (5) focus on inclusivity and capacity building. While there is no standardised approach to foster the 3S of healthcare innovations, a variety of practical frameworks and tools exist to support stakeholders along this journey.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | medium |
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 | 0.035 | 0.121 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.029 | 0.031 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".