Bibliographic record
Abstract
Background With over 120 local care providers, more than a quarter are currently rated by the Care Quality Commission (CQC) as Inadequate or Requires Improvement. Following our own Outstanding inspection we, as the sole provider of specialist end of life care, now have a unique role to play sharing our knowledge and skills. Aim The key to a successful CQC inspection is to work in partnership with other organisations with the aim of improving or maintaining CQC ratings to ‘Good’. A shift in culture and leadership will improve the lives and experiences of some of our most vulnerable people and place them at the centre of their care. Methods The programme aims to provide information and tools to help the proprietors and managers both produce and take forward robust quality assurance and action plans, focusing on culture and leadership based on the Key Lines of Enquiry, but particularly ‘Safe’ and ‘Well Led’. A five-day classroom based education delivered to four cohorts per year; access to an online ‘Share-point’ of information for all participating providers; and ongoing support visits to help implement the tools. Results With a rapidly subscribed programme extending over three years, there is already evidence of improved relationships and communication between Statutory Authorities and providers. Ratings are already improving as we share this hospice’s mission, vision and values to support each other. Conclusion With support and improved CQC inspections there will be fewer closures which result in the local area losing essential care provision. Working in partnership with an open and honest culture is the only sustainable model to ensure the future delivery of high quality care. Highlighting gaps in training and education this project has led to further programmes of training which we are now coordinating as a partnership to ensure the delivery of quality standardised education to all.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.399 | 0.190 |
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".