Nurse Educators’ Experiences Regarding Subject Competence at a Nursing College
Bibliographic record
Abstract
Nursing colleges, when faced with the difficulty of obtaining suitable candidates for vacant teaching posts, often assign the remaining nurse educators to teach in the understaffed areas even though they lack expertise in that particular subject. The purpose of this study was to gain an understanding of the experiences of nurse educators regarding their subject competence and to make recommendations to facilitate effective teaching and learning at a nursing college. A qualitative, phenomenological research design was used and a total of 20 nurse educators who complied with the inclusion criteria were recruited through purposive sampling. Audiotape recorded phenomenological, individual interviews were conducted and the collected data were analysed using Tesch’s protocol of qualitative data analysis. Ethical considerations were adhered to and trustworthiness was ensured. The three themes that emerged were that participants experienced: (1) incompetence, (2) inadequate didactic facilitation skills, and (3) defence mechanism. It is therefore recommended that nurse educators be empowered first in the subject they must teach to improve their self-esteem and teaching skills and as a result, are enabled to facilitate meaningful learning.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".