An Examination of Block and Integrated Practice Learning Models within Employer Sponsored Pre-Registration Nursing Programmes
Bibliographic record
Abstract
United Kingdom [UK] pre-registration nursing programmes [PRNPs] allocate half their curriculum learning hours to practice-based education which predominantly involves supervised workplace experience based on a block and/or integrated practicum model. Nevertheless, remarkably little robust research investigates the relative effectiveness and strengths/shortcomings of these models. br/brbr/br This mixed-methods study examines block and integrated placements within employer-sponsored BSc (Hons) PRNPs provided by a UK-based distance learning university. Beliefs/experiences of the two practicum designs shared by 37 respondents from four stakeholder groups were acquired via semi-structured interviews. Quantitative analysis involving a sample of 460 nursing students was also undertaken to ascertain whether exclusive exposure to one placement type affected withdrawal/achievement rates. br/brbr/br The research question for this investigation was: i‘What effect does a sponsor’s decision to adopt an integrated or block model of practice learning for those PRNP students whom they employ as non-registrant carers have on the student learning experience and retention/achievement?’/i.br/brbr/br Qualitative content analysis of the interviews yielded five common themes. Most importantly, respondents perceived the block model as more effective in promoting affinity, facilitating role transition, and mitigating against perceived difference, although the integrated framework was deemed preferable for services releasing students from their non-registrant carer work. Based on these results, critical situational factors requiring consideration when selecting the most appropriate practicum model are identified.br/brbr/br Subsequent crosstabulation and multinomial logistic regression analyses failed to demonstrate any statistically significant relationship between placement structure and student retention/degree classification. Finally, qualitative study data were re-analysed against the theory of human relatedness [THR]. It is argued that stakeholders may form their view of specific placement models by implicitly evaluating them against criteria akin to the key THR propositions. Improving practicum experiences might, therefore, also necessitate revising learning environment audit tools, challenging stakeholder practicum design prejudices and changing emphasis within PRNP curricula to better address these propositions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".