“Yes, the type of student supervision matters, but what about the placement structure?” - A critical realist review of block and integrated practice learning models within pre-registration nursing programmes
Bibliographic record
Abstract
This paper highlights the importance of effective clinical experiences for pre-registration nursing students and the wealth of published work associated with practice learning, particularly regarding approaches to student supervision during a practicum. It draws attention to frequent calls within nursing literature for longer placements; many of which fail to either identify the perceived benefits of such change or state whether a longer practicum should involve increased practice learning hours or redistribute existing hours over an extended period; key omissions given the resource-intensive nature of providing these educational opportunities. It also highlights a paucity of research regarding the effect of placement duration and intensity on clinical learning and that practicum design is commonly shaped by custom, practice, operational and financial considerations rather than a sound educational rationale. A Critical Realist review of studies associated with two fundamental placement structures, the block, and integrated models, is offered to consider their strengths and limitations. Moreover, work that evaluates initiatives offering students paid employment in caring roles undertaken alongside a pre-registration programme and therefore displaying similarities to the integrated practice learning model are examined. The review concludes that, as yet, there is insufficient empirical evidence to recommend the targeted application of either a block or integrated placement model within any specific part of a pre-registration nursing programme, calls for greater consistency in the language of placement structure and outlines the author’s own current work contributing to the extremely limited body of knowledge available regarding this aspect 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 | 0.068 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".