The use of block and integrated practicum structures within employer-sponsored pre-registration nursing programmes: A Critical Realist study
Bibliographic record
Abstract
This study, underpinned by Critical Realism, explores the use of block and integrated placement frameworks within employer-sponsored pre-registration nursing programmes at a United Kingdom university. Digitally recorded, commercially transcribed semi-structured interviews involving four stakeholder groups (employers, students, mentors, and practice tutors), were exposed to qualitative content analysis, and yielded four common themes; connectedness, role transition, carer work and difference. Most respondents perceived the block model as being more effective in promoting connectedness, facilitating role transition, and mitigating against perceived difference; although use of the integrated model was considered more desirable for services having to release these students from carer work. Results also highlight various factors which may influence the most appropriate choice of practicum model, including individual student characteristics, the service in which learners undertake their non-registrant care work, the nature of the placement and mentor autonomy within their clinical role. Congruent with the principles of Critical Realism, efforts to establish potential underlying causative mechanisms associated with practicum experiences are underway and currently involves scrutiny of these results against key features of the Theory of Human Relatedness. Furthermore, a regression analysis to identify the statistical relationship between the placement model completed by two national cohorts and retention rates/degree classifications is in progress. This combined work contributes to the extremely limited body of knowledge in an important area of curriculum design within nurse education.
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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 | 0.022 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".