Children’s views of nursing and medical roles: implications for advanced nursing practice
Bibliographic record
Abstract
AIM: Changes in healthcare delivery make it increasingly likely that children accessing ambulatory care will receive their health assessment and management from nurses rather than junior doctors. As part of a larger study exploring the safety and efficacy of nurse-led pre-operative assessment (Rushforth et al 2006) this study aimed to discover children's views of nursing and medical roles. METHOD: Data were collected from 63 children using drawing and writing activity sheets during preadmission events. FINDINGS: Findings suggest that there is a clear demarcation in children's minds between doctors and nurses. In addition to the gender differences, children saw 'caring' as a nursing role and 'curing' as a medical role. However, there has been some change since earlier studies with only three children noting that nurses 'helped doctors' or 'did what doctors tell them' and less than a quarter of the children drawing nurses with hats. CONCLUSION: As with all other patients, children should be fully informed of the status of the practitioner undertaking their care; understanding how they perceive the roles of doctors and nurses will support this information giving.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".