Nurse educators’ reflections on factors that contributed to their resignation at a public nursing college in Johannesburg, South Africa
Bibliographic record
Abstract
Background and objective: There is a shortage of nurses in the country and worldwide, and the problem is compounded by the resignation of nurse educators. When nurse educators resign, they leave with their expertise and skills, thus compromising the provision of quality teaching and learning in the institution. It is imperative that a study to determine the factors contributing to the resignation of nurse educators be conducted. The aim of the study was to explore and describe the factors that contributed to the resignation of nurse educators at a Johannesburg nursing college in South Africa. Methods: A qualitative, exploratory, descriptive and contextual research design was used to provide an in-depth description of factors that contributed to nurse educators resigning from a Johannesburg nursing college. Individual semi-structured interviews were conducted with 15 purposively selected nurse educators, using audiotapes until data saturation. Data were analysed by the researcher and an independent coder using the Tesch protocols on thematic analysis. Trustworthiness was achieved using Lincoln and Guba’s strategies.Results: Three themes emerged, namely: experience of an unappreciative working environment; negative influences on the ‘self’ of the nurse educator; and the need for career advancement and professional growth.Conclusions: The provision of quality nursing education to produce nurses will be difficult in the face of nurse educators resigning from their posts. There is a need to implement retention strategies to create an appreciative working context for nurse educators in the institution.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| 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".