46 enhance: supporting integrated care systems in meeting their local healthcare priorities and workforce development needs
Bibliographic record
Abstract
Introduction Integrated care systems (ICSs) were established in 2022 to meet the needs of local populations ICSs have responsibility for workforce development of health and care staff in the locality NHS England’s enhance programme is an educational development offer, applicable to all health and care staff (including non-clinical), with emphasis on system working, health inequalities, environmental sustainability and population health, aligning to ICS priorities Learners are encouraged to develop their leadership skills through service improvement projects and collaborative learning activities. Aims and objectives of the research project or activity enhance aims to address the educational requirements that can support sustainable workforce planning and delivery of integrated person-centred care through initiatives which prioritise staff wellbeing and self-directed professional development. Its flexible place-based offer aims to allow ICSs to address their local health priorities whilst encompassing the values of enhance and developing and retaining their workforce. Method or approach enhance was piloted by seven regional trailblazers, with two (NEY and SW) closely aligned to local ICSs. Tailored offers were developed for multi-professional teams covering different geographical footprints. Mixed methods delivery included expert presentations, access to online learning resources, Action Learning sets, community and cross-sector ‘field trips’, and quality improvement projects. Building on this success, ICSs across England were given the opportunity to bid for funding to run their own place-based enhance offer. Two ICS pilot bids in Norfolk & Waveney and Kent & Medway were accepted and will launch in 2024. Further ICS bid submission opportunities will be available in 2024. These will use enhance domains and values to encourage and explore system working, and to engage and equip the workforce to address local priorities. Findings enhance delivered effective and enjoyable multi-professional training to clinical and non-clinical health and care workers, breaking down professional silos. Feedback (qualitative and quantitative) showed improvements in knowledge, skills and attitudes about health and care systems and teams. Participants gained accredited CPD with some progressing to PGCert. Quality improvement projects delivered benefits for patients, teams and the environment, whilst developing leadership skills for learners. The NHS Long Term Workforce Plan (LTWP) has identified expansion of enhance programmes to ICSs as a priority to further develop generalist skills for all health and care professionals. Key messages Enhancing generalist skills within the workforce is critical in managing increased patient complexity in a changing healthcare environment, as highlighted by the NHS LTWP. enhance trailblazer pilots demonstrated that learning across multi-professional teams, based on local population health needs, can enhance the skills of the local workforce and deliver service improvements whilst developing generalist skills. Further expansion through ICS pilots will provide more place-based offers tailored to local health needs which prioritise patient care and professional development of staff.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".