Facilitators that assist teams move to a clinical network model of service delivery from the perspective of those working within a network: a scoping review
Bibliographic record
Abstract
Purpose A recent innovation towards achieving integrated care is the use of clinical networks to promote the coordination of services across organisations. The objectives of this scoping review are to understand the facilitators and barriers to clinical network formation from the perspectives of the clinicians, network managers and those with health policy or planning responsibilities. Design/methodology/approach A review of the available literature was conducted, informed by the Joanna Briggs Institute Guide and the Preferred Reporting Items for Systematic reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) Checklist. Findings were mapped using the Socio-technical systems theory. This review focused on studies that undertook an empirical analysis of the perspectives of those involved in clinical networks. Findings The findings identify a core set of conditions to facilitate network success: (1) effective leadership and management, (2) achieving engagement in network activities from those within and external to the network, (3) the network’s structures and processes encompassing factors such as the governance style, communication strategies and metrics to demonstrate outcomes and (4) adequate resources including staffing and the technology to support network activities. Practical implications While a network approach is being championed worldwide, guidance on how to implement it in practice is still evolving. These findings identify a core set of preconditions to facilitate clinical network success. Originality/value To our knowledge, these findings provide a unique overview of all studies conducted to date which explore the contributors to network success from the perspectives of those involved in network activities.
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 | 0.138 | 0.266 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.021 | 0.015 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".