28 What competency frameworks are available to promote a consistent education framework for the palliative and end of life care workforce in Wales? A rapid evidence map
Bibliographic record
Abstract
Introduction Education frameworks identify specific learning needs, promote consistent, inclusive and flexible approaches to education, address discipline-specific standards and support learning and development at individual, service, and organisational levels. A recent service evaluation in the Cardiff and Vale University Health Board (C&V UHB) identified areas of inequity regarding standardised palliative and end of life care (PEOLC) education. Furthermore, the National Programme Board for Palliative and End of Life Care (NPBPEOLC) in Wales has prioritised the need for an all-Wales strategy to identify an established competency framework or develop a framework specific to Wales. Aims To conduct a rapid evidence map on behalf of NPBPEOLC to identify established PEOLC education frameworks from the published literature and map the core domains and competencies included within them. Methods Four key databases were searched from 2012–2022 for relevant published papers. Reference lists of systematic reviews were checked for appropriate studies. Methodology was used from the Palliative care Evidence Review Service (PaCERS)1 for this review, with some adaptations. Results Of 84 articles identified, 8 studies met the inclusion criteria. Two frameworks were based in the UK (Scotland and England), 3 in the USA, 2 in Canada and 1 in Ireland. A mapping exercise was carried out, whereby competencies from identified frameworks were mapped to the European Association for Palliative Care (EAPC) Ten Core Competencies. Two multidisciplinary, comprehensive frameworks covered all ten EAPC domains across their competencies.2 3 Conclusions The findings will be used by the NPBPEOLC to inform a Wales specific PEOLC education core competency framework. Impact The frameworks identified: provide sufficient coverage of competencies to inform a Wales-wide multidisciplinary competency framework for adult specialist palliative care. contribute to providing a standardised training framework for organisations to implement, regulate and continuously evaluate. References Mann M, Woodward A, Nelson A, Byrne A. Palliative care evidence review service (PaCERS): a knowledge transfer partnership. Health Res Policy Sys 2019;17(1):100. https://doi.org/10.1186/s12961-019-0504-4 Health Education England. 2017. End of life care core skills education and training framework. Available from: https://www.skillsforhealth.org.uk/wp-content/uploads/2021/01/EoLC-Core-Skills-Training-Framework.pdf. Accessed 5 Oct 2022. Connolly M, Ryan K, Charnley K. Developing a palliative care competence framework for health and social care profession-ALS: the experience in the Republic of Ireland. BMJ Supportive & Palliative Care 2016;6(2):237–242. https://doi.org/10.1136/bmjspcare-2015-000872
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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 | 0.105 | 0.205 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.025 | 0.013 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 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".