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Record W4417115400 · doi:10.1016/j.jaging.2025.101381

Aging, death, and dying: Perspectives of older persons in the eastern region of Ghana

2025· article· en· W4417115400 on OpenAlexaff
Mavis Dako‐Gyeke, Kwamina Abekah‐Carter, Richard Baffo Kodom, F. Akosua Agyemang, Vyda Mamley Hervie

Bibliographic record

VenueJournal of Aging Studies · 2025
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsUniversity of ManitobaMemorial University of Newfoundland
Fundersnot available
KeywordsFocus groupQualitative researchGovernment (linguistics)Grounded theoryOlder peopleWelfareLife course approachQualitative property

Abstract

fetched live from OpenAlex

The meanings older persons ascribe to aging, death, and dying vary across cultures; hence, the need to engage them to share their perspectives on these concepts. Drawing on the life course theoretical perspective, this study explored the beliefs of older persons about aging, death, and dying at Akropong and Adukrom in the Eastern Region of Ghana. Utilizing the qualitative research design, data were gathered from 34 older persons through in-depth interviews and focus group discussions. The data were transcribed and analyzed thematically, and the findings showed that aging is a life process that entails changes in a person's physical and mental capacities, as well as social roles. Additionally, participants shared their notions about aging well and the benefits of aging. Regarding death and dying, the participants indicated that these were inevitable phases of life and further made distinctions between good death and bad death. Moreover, they described uncertainties surrounding the dying process and the preparations they had made towards dying. The findings provide insights that could inform government policy, social work, and social welfare systems to support and enhance the well-being and self-worth of older persons in Ghana and beyond. This can be achieved through the provision of culturally grounded and contextually relevant care and interventions.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.317
Threshold uncertainty score0.212

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.086
GPT teacher head0.429
Teacher spread0.343 · 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 teacher head, 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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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