Assessing challenges of clinical education in a baccalaureate nursing program in Ghana
Bibliographic record
Abstract
Background: A 2010 study of preceptorship as a clinical teaching model in Ghana revealed weaknesses related to high student-preceptor ratios and inadequate support from faculty in the educational institution. A four-cycle community-based participatory action research study was designed to further delineate clinical teaching and learning issues and partner with Ghanaian stakeholders in critical analysis of possibilities for positive change in clinical nursing education. The purpose of this paper, taken from Cycle One of the study, is to provide understanding of the challenges of the current clinical teaching model(s) used in the study institution from the perspectives of students and faculty. Early engagement of external stakeholders is described.Methods: Each university target group was invited to complete a semi-structured questionnaire. Interviews were conducted with representatives from the Ministry of Health, the Nursing and Midwifery Council of Ghana, and the Ghana Registered Nurses’ and Midwives’ Association. Clinical documents were examined.Results: Clinical teaching and learning issues identified included the need for: a) more effective clinical teaching and supervision; b) adequate equipment for practice; c) meaningful evaluation of performance; d) enhanced collaboration between the school and clinical settings; and, e) reduced travel time to clinical opportunities. External stakeholders became aware and supportive of the research endeavour.Conclusions: Participants acknowledged changes are needed in order to improve clinical nursing education in Ghana. Clinical teaching and learning issues were identified and formed a baseline from which more in-depth discussion of resources, constraints and possibilities for change could ensue in subsequent cycles of the study.
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".