Evaluating the effectiveness of a practice nurse development programme in Tower Hamlets, London
Bibliographic record
Abstract
Aim: This evaluation sought to independently evaluate the effectiveness of a Practice Nurse development programme including its impact upon capacity, access, recruitment, retention and perceived improvement for increased patient quality of care. Nurses are a significant and growing component of general practice. There is a need to support innovative education leading to increased retention and role expansion particularly in areas of high health needs. Method: Mixed methods of comprising interviews and focus groups were undertaken and data were analysed using thematic analysis. Participants comprised of Practice Nurse trainees, General Practitioners, practice staff, programme facilitators and a programme mentor (n=21). Results: Findings indicate this programme produces nurses with structured, up- to- date competence-based knowledge for effective deployment of staff. Conclusions: This new programme can be utilised as a means of producing and retaining competent committed practice nurses whilst increasing capacity and delivering high quality care. Its approach may provide a positive future model for efficiently and speedily training practice nurses whilst increasing their competencies and depth of knowledge.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".