HELPING TO SHAPE CQC INSPECTIONS OF SPECIALIST CHILDREN'S TRUSTS
Bibliographic record
Abstract
Aim To participate in a pilot Care Quality Commission (CQC) inspection of a specialist paediatric hospital and to provide feedback to improve further inspections. Method To meet the CQC9s regulatory requirements, the methodology of the inspection process had been improved and refined. A ‘peer-review’ model of inspection was now employed, utilising larger more inclusive teams to deliver a more in-depth inspection in a shorter time-frame (2 days). The inspection team consisted of general and specialist paediatricians, general and specialist children9s nurses, pharmacists and other Allied Healthcare Professionals (AHPs), play therapists, managers and experts by experience (parents), supported by experienced CQC inspectors and data analysts. A training day prior to the inspection enabled the team to meet, become acquainted, learn about the CQC, understand individual roles and establish an inspection schedule. The inspection itself took the form of ward visits, individual interviews with key personnel, small group interviews, staff focus groups, observing care and speaking to service users and a public listening event was also held. Results The inspection-pharmacist was tasked with interviewing the chief pharmacist, leading a peer focus group of AHPs, conducting informal interviews with service staff, recording ward and service-provision observations, and providing expert advice to team members on medicines management issues. To ensure a consistent approach to inspections, all Trusts are assessed against the CQC9s five domains (safe, effective, caring, responsive and well-led); for each domain there is a generic set of ‘Key Lines of Enquiry’ (KLOE). During the inspection, twice daily corroborative meetings were held to allow team members to discuss emerging findings and any common themes causing concern. All collected pieces of evidence were rated against the KLOEs as objectively as possible. All aspects of medicines management were viewed: pharmacy work force, governance, error reporting, information provision to staff and parents, storage and procurement, safe practice and patient perception. At the end of the inspection the team was debriefed and feedback was collated and assimilated. This would be used as evidence in the inspection report. Conclusion The CQC inspection was fast-paced and very intensive with a huge amount to cover in a very short space of time. In addition to the feedback given during the inspection, a pharmacist9s summary report was submitted which documented the positive aspects of the investigation, but also made suggestions for further improvement. It was noted that medicines management should be inspected across the hospital as a speciality in its own right as it impacts on many aspects of hospital service. The inspection team could have benefitted from having two pharmacists, each focussing on different aspects of medicines management. The paediatric pharmacist proved to be a valuable member of the inspection team, providing expert advice on medicines management issues in children, as well as identifying and highlighting areas normally associated with the greatest risk within the paediatric population.
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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 | 0.064 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".