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Record W4416925399 · doi:10.1371/journal.pone.0334404

An explanatory sequential mixed method study of nursing students’ self-efficacy in caring for older adults in Ghana

2025· article· en· W4416925399 on OpenAlexaff
Diana Abudu-Birresborn, Martine Puts, Lynn McCleary, Charlene H. Chu, Vida Nyagre Yakong, Lisa Cranley

Bibliographic record

VenuePLoS ONE · 2025
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsUniversity Health NetworkToronto Rehabilitation InstituteUniversity of TorontoBrock University
Fundersnot available
KeywordsGerontological nursingLeverage (statistics)MEDLINENurse educationNursing practiceNursing homes

Abstract

fetched live from OpenAlex

BACKGROUND: There is considerable evidence indicating that nursing students demonstrate inadequate knowledge and negative attitudes toward working with older adults. This suggests nursing student's unpreparedness to provide care for the expanding older adult population. Feelings of unpreparedness can negatively impact their motivation and confidence. However, limited evidence exists about how nursing students' knowledge and attitudes influence their self-efficacy in caring for older adults. Knowing this can help to identify gaps and opportunities to facilitate nursing students' confidence in caring for older adults in acute care settings. AIM: To examine nursing students' knowledge, attitudes, and self-efficacy and how these variables impact nursing student self-efficacy in caring for older adults in acute care settings in Ghana. METHODS: We employed explanatory sequential mixed method approach. In Phase I, we used a cross-sectional design and collected quantitative data about students' knowledge, attitudes, and self-efficacy. Data were collected from 170 second and third-year nursing students between December 2019--March 2020. We analyzed the data using descriptive and multiple-variable linear regression. Survey results informed the selection of students for Phase II based on their scores. In Phase II, 17 nursing students were purposively selected for semi-structured interviews between November and December 2020. Interviews were transcribed and analyzed using thematic analysis. Both results were integrated and presented. RESULTS: Students' mean age was 21 years (SD = 3.73). Just over half were female (54%). The majority had lived with/were currently living with older adults (83.0%). Many had low knowledge scores (71%) and a majority had positive attitudes (91%) and high self-efficacy scores (97%). Nursing students' ages and attitudes were significantly positively associated with their self-efficacy. There was no significant association between students' gerontology knowledge and self-efficacy. Qualitative findings showed that low knowledge scores were due to limited attention to gerontology education in the curriculum and heavy course load. Sociocultural norms in caring for older adults influenced students' positive attitudes. This facilitated students' interactions with older adults and increased their confidence. Higher self-efficacy scores were associated with the impact of the general nursing program, students' perceived familiarity with the needs of older adults and routine procedural knowledge. Younger students perceived that their age and competencies were questioned by older adults, impacting their self-efficacy. Both datasets converged at integration. CONCLUSION: It is imperative to enhance students' knowledge and leverage their self-efficacy to advance gerontological nursing education and practice in Ghana.

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 imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.012
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.107
GPT teacher head0.449
Teacher spread0.342 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations1
Published2025
Admission routes1
Has abstractyes

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