A MIXED METHOD APPROACH TO UNDERSTANDING NURSING STUDENTS' SELF-EFFICACY TO CARE FOR OLDER ADULTS IN GHANA
Bibliographic record
Abstract
Abstract To identify students’ motivation, gaps, and opportunities to design effective teaching strategies, it is necessary to examine nursing students' understanding of their self-efficacy to care for persons 60+ years. A mixed-method approach was employed. In phase 1, 170 second-and third-year nursing students from two public Nursing Colleges in Ghana were recruited. A cross-sectional survey included the General Self-efficacy to Care for Older Adults’ scale (GSE-COA), the Kogan’s Attitudes towards Old Peoples scale, and the Knowledge about Older Patients Quiz. Data were analyzed using descriptive statistics in SPSS IBM 26. The results of phase 1, informed the selection of students for phase 2. In phase 2, 17 students were purposefully selected for in-depth semi-interviews. Qualitative data were analyzed using thematic analysis. Both results were then integrated, interpreted, and presented jointly. Students’ mean age was 21yrs (SD=3.73), with 91 (54%) females. Most students 140 (71.0%) had lived with/were currently living with an older adult. The majority 164 (97%), had higher scores for GSE-COA (mean= 107, SD=14.29), indicating high self-efficacy. Qualitative results showed that students’ high self-efficacy was due to their familiarity with older adults at home and their perceived competence in routine nursing care. Students who demonstrated a high sense of self-efficacy were confident and perceived caring for older adults as a duty and responsibility, had experience/exposure to and were comfortable working with older adults. The findings demonstrate an opportunity to design effective teaching strategies to develop and sustain students’ interest and motivation in the care for older adults in Ghana.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".