P-197 Recruiting and retaining high quality staff – innovative solution to succession planning
Bibliographic record
Abstract
Background The ability to deliver high quality compassionate end of life care depends on recruiting and retaining high quality staff. Post COVID-19, organisations including hospices acknowledge significant national challenges in recruiting clinical staff with appropriate palliative care experience to meet increasingly complex needs. Innovative ways of succession planning are essential. We present an innovative project whereby aspiring Nurse Consultants are recruited to join an educational programme, incorporating the five pillars of advanced practice, underpinned by robust capabilities. We invite aspiring nurse consultants from other palliative care organisations across the UK to join the programme to benefit from shared learning and development. It encourages co-creation of learning opportunities with the aspiring Nurse Consultants. Aims In our presentation we will explore the advantages/challenges of developing a contemporary programme of education and support for aspiring Nurse Consultants, as a prototype to develop consultant nurses of the future who can fill the requirement for consultant level input to hospice and broader palliative care workforces. The paper considers how this programme might be upscaled. Approach An initial cohort of three aspiring Nurse Consultants at St Christopher’s will be joined by further candidates to start a formal programme of learning, augmented by in-role support and reflection. All attend to clinical practice, service development, leadership, research and QI. The detail of the approach reflects seminal work by Manley, Taylor and Canadian capabilities related to the delivery of palliative care. Results We will report on progress related to recruitment, the detail of the programme, development of the community of practice to which all participants will join and any learning along the way. Also, our insights regarding the value of such a course to support the increasing need for high quality end of life care as detailed in the Long-Term Plan and the Health and Social Care White Paper.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".