The education and training needs of advanced clinical practitioners: An exploratory, qualitative study
Bibliographic record
Abstract
Advanced clinical practitioners increasingly provide patient care in a variety of settings across the world. This paper reports a qualitative study exploring the training and education needs of these healthcare professionals in England. Four focus group discussions and one individual interview were conducted with a total sample of 17 people. Participants were adults enrolled on an Advanced Practice Masters programme at one Higher Education Institution or advanced clinical practitioners from across two large London National Health Service hospitals. Data collection took place March-April 2017. Following transcription, audio-recorded data were imported into Nvivo11 and subjected to a standard process of inductive thematic analysis. Three key themes were identified: Recognising advanced practice; Education for Advanced Practice; Programme delivery. Findings highlight the huge variation in titles, practice responsibilities and management structure, which make the development of a generic education programme challenging and the importance of flexibility key to success. At a time when public services are experiencing significant financial constraints, the need for improved collaborative practices, shared resources and a practice focus is considered vital for educating future advanced clinical practitioners worldwide.
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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".