GHANAIAN NURSING STUDENTS’ EXPERIENCES IN CARING FOR OLDER ADULTS IN ACUTE CARE SETTINGS
Bibliographic record
Abstract
Abstract Evidence suggests that nursing students’ experiences in acute care settings may negatively impact their views on caring for older adults, resulting in poor interest and quality of care. However, Ghanaian nursing students’ experiences caring for older adults are unknown. We aimed to explore students’ experiences in caring for older adults to help develop and improve strategies, that promote students’ positive experiences and promote quality of care for older adults. We employed a descriptive qualitative approach and interviewed 17 second and third-year nursing students since they had clinical exposure. Interviews were conducted face-to-face and via telephone from November 2020- December 2020 and lasted 45-60 minutes. Interviews were audio recorded, transcribed, coded, and sorted with Nvivo. Data were analyzed and interpreted, themes were generated using critical thematic analysis and contextual meanings students ascribed to their responses. Two broad themes were generated. 1) experiences in caring for older adults and 2) inadequate staff and resources. Students had challenging experiences caring for older adults who were dependent and unable to manage their activities of daily living. They described caring for older adults as physically and emotionally demanding. They noted that limited staff and resources constrained caring for older adults which frustrated them when providing care. However, they expressed caring for older adults as a positive learning experience as they perceive them as a source of wisdom and blessings. Nurse leaders and administrators can help shape students’ experiences caring for older adults by ensuring adequate staff and resources and supporting students’ clinical learning.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".