UK Consensus Project on Quality in Palliative Care Day Services
Bibliographic record
Abstract
The UK Consensus Project on Quality in Palliative Care Day Services is funded by MarieCurie and based at Queen’s University, Belfast. It is being carried out in collaboration with anumber of other academic institutions and key stakeholder organizations. The project willinvolve and take into account the perspectives of a range of different stakeholders and itsoutcomes will be relevant to patients, family members, palliative care professionals, andpolicy makers. The aim is to use an expert consensus process to develop a set of qualityindicators for assessment of all aspects [structure, process and outcomes] of quality of care in Palliative Care Day Services. This expert panel will include around 20 people from all over the UK who work in, or have experience of day services. The panel will include a wide range of different professionals and volunteers, as well as people who have experience of attending day services as a patient, a carer or family member.Agreement on a quality indicator set will be reached using the RAND/UCLA Appropriateness Method [1], a method of reaching consensus based on differing opinions. The first stage of the process will involve a panel of 20 experts individually considering and rating the appropriateness of potential quality indicators. Ratings will be based on their own experience and knowledge, as well as a series of evidence summary tables drawn up based on a systematic review carried out to identify existing indicators relevant to palliative care day services. Ratings will be analysed and summarised in advance of the second round, at an expert panel meeting. At this meeting quality indicators and their preliminary ratings will be discussed and re-rated. All ratings will be made anonymously. Unlike other consensus development processes, complete agreement is not required. During the meeting, discussions will be focused specifically on any areas of disagreement, in order to understand how and why the variation was found. Indicators will be considered to be “appropriate” if they have a median rating of 7 or more without disagreement. Disagreement will be defined as more than 30% of panel members give a rating of 3 or less to a potential indicator. An indicator will be considered as “not appropriate” if its median score is 3 or less without disagreement (disagreement being when 30% or more of the ratings are 7 or more). When the median rating is between 4 and 6, or if disagreement is observed, the appropriateness of the indicator will be considered as uncertain and it will be discussed at the panel meeting. The following options will be offered to the panel members: acceptance of a quality indicator, rejection or adjustment of a quality indicator, and merging multiple indicators into a single quality indicator. Panel members will also be asked to identify additional indicators not on the original list, modify existing indicators that may require re-wording and remove those perceived to be irrelevant. Following this discussion, a revised list of indictors will be developed, and panel members will re-rate the appropriateness of each. Panel members will also be asked to rate the feasibility or practicability of measuring each indicator in a typical day service setting using the same 9-point scale. At the end of the process, a set of quality indicators will be developed that passed the first round of individual rating as well as the second-round discussion. This set of quality indicators will be sent to the expert panel by e-mail after the meeting, as well as to other stakeholders, for final approval. Following this, further work will be conducted to develop and test the suitability of a toolkit which can be used to measure quality indicators in palliative care day services.
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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 | 0.194 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".