Medical education and informal teaching by nurses and midwives
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to examine the contribution of nurses and midwives to the education of medical colleagues in the clinical context. METHODS: The research design was a cross-sectional survey using an online questionnaire. A subsample of 2906 respondents, from a total of 4763 nurses and midwives participating in a web-based study, had taught doctors in the 12 months prior to the survey. The questionnaire generated mainly categorical data analysed with descriptive statistics. RESULTS: In the group of respondents who taught doctors (n =2906), most provided informal teaching (92.9%, n=2677). Nearly a quarter (23.9%, n=695) self-rated the amount of time spent teaching as at least moderate in duration. The most common named teaching topics were documentation (74.8%, n=2005) and implementing unit procedures (74.3, n=1987), followed by medication charting (61.9%, n=1657) and choosing correct medications (55.8%, n=1493). Respondents felt their contributions were unrecognised by the doctors and students they taught (43.9%, n=1256). CONCLUSIONS: Educational contributions while unrecognised could be considered positively by the respondents. However, discussion of teaching responsibilities is necessary to support the development of teaching protocols and supervision responsibilities as respondents reported teaching clinical medical tasks related to medications, consent and other skills within the medical domain. Study limitations include the nature of self-reported responses which cannot be validated and data drawn from a survey concluded in 2009.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".