Perspectives on transfer of learning by nursing students in primary healthcare facilities
Bibliographic record
Abstract
Supportive work environments promote professional socialisation and integration of theory in practice. Qualitative data generated through four nominal groups were deductively analysed using the components of the systemic model of training transfer. This article reports on the perspectives of nurse clinicians, clinical facilitators, and students from the training institution regarding aspects of student characteristics, educational design, transfer climate and work environment that influence nursing students’ transfer of learning in primary healthcare (PHC) facilities. A perception exists that students lack the desire to use knowledge and skills mastered in the training programme in clinical practice. Although the educational design strives to promote transfer of classroom learning, students may not be motivated to transfer classroom learning. The learning climate hampers transfer of learning because the students’ perceptions are that they are unwelcome, not taken into consideration and not respected. The lack of essential equipment demotivates students. This study confirms the interrelatedness of the systemic transfer of training model and emphasises the importance of considering all elements that influence learning transfer when planning clinical placements of students.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| 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".