Bibliographic record
Abstract
IntroductionIn this workshop we will share lessons learned from Canada, England and the Netherlands on how innovations or good practices in integrated care can be widely spread and implemented. We will depict how adopters of innovations can be supported in capacity building. The success of system-level spread is contingent on a large number of well supported and committed adopters.BackgroundOften, it is assumed that national/regional policy measures and structures, such as legislation, funding and types of organisation, explain successful or failing implementation of proven innovations. However, Horton et al. (2018) made a compelling argument that strategies to support the spread and scale of complex innovations should respect and support the role of the adopters. Integrated health and social care programs are social, context sensitive and dynamic. Therefore, implementing complex innovations in other settings requires these innovations to be described in a way that is useful to the intended adopters, as well as paying attention to commitment and capacity building of these adopters. We analysed three national implementation programs according to the principles articulated by Horton and colleagues. In each country, various ways to actively engage and support large numbers of adopting organisations were deployed to make innovations work in their context. Capacity building and encouraging commitment in adopters appeared to be crucial. Based on this analysis, we argue that removing policy barriers and establishing incentives is necessary, but that supporting and committing adopters is another crucial element of spread.Aims and objectivesIn this workshop we actively exchange lessons learned of the three programmes and of the participants of the workshop in terms of key mechanisms for large scale implementation of innovations in integrated care. We aim at learning about scaling up and spreading innovations or good practices across different settings. Target audiencePolicy makers, managers, support staff and commissioners at national, local or organisational level, as well as health management researchers.Learnings/Take awayWe will share how in practice large scale implementation and mutual learning can be organised. The lessons will be transferable to participants’ specific contexts.FormatWe will depict an analytical working model to describe the elements of successful spread (10 minutes). In short presentations, we will demonstrate how the principles of this model were applied in practice in three national programs (aiming at better care closer to home and community, quality and efficiency in integrated long-term care and vanguards in integrated care) (25 minutes). We will share experiences of participants on local implementations, thereby aiming to complement and validate identified mechanisms and translate them to practical guidelines for everyday practice. A checklist will be provided as a helpful tool (40 minutes). Finally, the experiences and outcomes will be briefly discussed in the plenary (15 minutes).Preferred length90 minutesReferencesHorton TJ, Illingworth JH, Warburton WHP. Overcoming Challenges In Codifying And Replicating Complex Health Care Interventions. Health Aff (Millwood). 2018 Feb;37(2):191-197. doi: 10.1377/hlthaff.2017.1161. Available from: https://www.healthaffairs.org/doi/full/10.1377/hlthaff.2017.1161
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 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 | 0.042 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.006 | 0.028 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".