Bibliographic record
Abstract
Background and objective: The provision of appropriate ‘pastoral’ support for nursing students is acknowledged to be problematic for a variety of reasons, (time constraints, staffing levels, unmanageable workloads). The need to initiate and access more suitable support is imperative – particularly in the light of the increasing number of students suffering with mental health issues. This study examines the dynamics of a student peer support programme over a two-year period. Twenty-one first year students (child field) gave fully informed consent to being involved in a peer support study. Nineteen second year students (again, child field) consented to being peer supporters for the junior students.Methods: The team, consisting of two academics and two clinicians, explored the relatively simple option of second year nursing students ‘peer supporting’ first year students in various aspects of their training over a two-year period – from social support, academic support, pastoral support and clinical support. An evaluation of the initiative was through a questionnaire at four separate intervals over the two-year period.Results: The results were hugely positive, and encouraging. Both cohorts of student found the intervention accessible, supportive, and sustainable. Peer support may be a relatively straightforward, and simple concept to assist junior nursing students in their often very complex, and overwhelming, transition.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".