16 Impact of gold standards framework accreditation on specialist palliative care referrals in acute hospital setting; addressing inequalities in access
Bibliographic record
Abstract
Background It is established that people with a non-cancer diagnosis tend to have less access to supportive and palliative medicine and may have a poorer experience of care in the last phase of their life and this inequality is acknowledged within current end of life care provision. At Dudley Group NHS Foundation Trust (DGFT) we have implemented the Gold Standards Framework (GSF) trust wide, with eight wards achieving GSF accreditation, with continuing engagement across the trust. This review aimed to understand the impact implementing the GSF has had on the hospital specialist palliative care team referrals. Method Using PowerBI data analysis a retrospective review of the proportion of referrals by diagnosis group was performed over 16 months from January 2022 to April 2023 alongside the number of referrals. Results The review identified an increasing trend in the proportion of patients referred with a non-cancer diagnosis. From as baseline around 25% non-cancer and 75% cancer there has been a clear increase in the non-cancer referral to a 50:50 split. During this 16-month timeframe there was also continued growth in the number of referrals, with the increase driven from the non-cancer diagnosis group with a 48% average increase in referrals per quarter, whilst cancer group referral numbers remained stable (3% average growth per quarter). Conclusion This review highlights the benefits of embedding the GSF on improving identification of patients and increasing access to specialist palliative medicine, particularly for non-cancer patients. As a Specialist Hospital Palliative Care service, the local response to increased recognition has included the involvement within local non-cancer multidisciplinary meetings. These findings support the benefits of embedding the GSF to improve upon inequality in access to specialist palliative care for non-cancer patients.
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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 | 0.061 | 0.247 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".