Understanding the Practices and Experiences of Supervising Nursing Doctoral Students: A Qualitative Survey of Two South African Universities
Bibliographic record
Abstract
Doctoral supervision involves an intensive, interpersonal one-to-one relationship between the supervisor and the student. Supervisors have a responsibility to guide students when choosing their research topics and throughout the research process until completion of their research projects. The purpose of this study is to explore the practices and experiences of faculty members supervising doctoral nursing students in two selected universities in South Africa. This qualitative and explorative study involves all faculty members supervising doctorate nursing students at four South African Universities in Limpopo Province. A purposive sampling was used to select 15 participants who met the inclusion criteria. Data collection was through a telephonic in-depth unstructured interview. Probing was used to elicit more information from participants. Data were analysed through Tesch’s open coding method. Findings reveal three themes as practices and experiences of supervision, namely: research supervisory role, knowledge of models of supervision, and guiding principles towards doctorate supervision. There is a need for orientation of research supervisors and doctoral students before they commence with their supervisory role. Policies and procedures for doctorate supervision need to be communicated to all supervisors. There should be continuous support for both supervisors and students during the process of supervision.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".