Being at Peace as an Important Factor in Acquiring Teaching Competency by Iranian Nurse Teachers: A Qualitative Study
Bibliographic record
Abstract
It is imperative to understand the factor that influence teaching competency. Therefore, it is necessary to study those that have an impact on the process of acquiring teaching competency. Competent nurse teachers have an important role in the achievement of nursing students and improving the quality of nursing education. However, few researches have focused specifically on the process of acquiring teaching competency in nurse teachers and its related factors. This study as a part of more extensive research aims to explore the factors influencing acquisition of teaching competency by Iranian nurse teachers. Grounded theory was chosen as the method. Eleven teachers from three nursing schools in Tehran were recruited. Data was generated by semi structured interviews during May 2011 to March 2013 and was analyzed through using constant comparison. Three main categories were emerged including "individual characteristics" (spirituality, professional interest, ethical conducts, knowledge expansion and reflective practice), "organizational factors" (management of educational systems, solidarity culture, student characteristics) and "socio-cultural factors" (social situations, and public definition of nursing). Nurse teachers who deal peacefully with the nursing profession and colleagues are responsible and committed to acquiring teaching competency. A suitable organization in nursing educational systems that is structured and ordered also encourages a peaceful approach by nurse teachers.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".