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Record W7133379698

Setting priorities for ageing research in Africa:A systematic mapping review of 512 studies from sub-Saharan Africa

2021· article· en· W7133379698 on OpenAlexaff
including Emerging Researchers and Professionals in Ageing-African Network, Michael Kalu, Blessing Ugochi Ojembe, Olayinka Akinrolie, Augustine C Okoh, Israel I. Adandom, Henrietta C Nwankwo, Michael S Ajulo, Chidinma A Omeje, Chukwuebuka Okeke, Ekezie M Uduonu, Chigozie Donatus Ezulike, Ebuka Miracle Anieto, Diameta Emofe, Ernest C Nwachukwu, Michael C Ibekaku, Perpetual C Obi

Bibliographic record

VenueCityU Scholars · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsUniversity of LethbridgeUniversity of ManitobaMcMaster University
Fundersnot available
KeywordsCategorizationObservational studyResearch designQualitative researchPopulation ageingNarrativeOlder peopleSystematic reviewContent analysis
DOInot available

Abstract

fetched live from OpenAlex

Background In 2040, the older population’s growth rate in sub-Saharan Africa (SSA) will be faster than those experienced by developed nations since 1950. In preparation for this growth, the National Institute on Aging commissioned the National Academies’ Committee on Population to organize a workshop on advancing aging research in Africa. This meeting provided a platform for discussing some areas requiring improvement in aging research in SSA regions. We believed that conducting a systematic review of peer-reviewed articles to set priorities for aging research in SSA is warranted. Therefore, this article is the first in a Four-Part series that summaries the types and trends of peer-reviewed studies in SSA. Methods This systematic mapping review followed the Search-Appraisal-Synthesis-Analysis Framework. We systematically searched multiple databases from inception till February 2021 and included peer-reviewed articles conducted with/for older adults residing in SSA. Conventional content analysis was employed to categorize studies into subject-related areas. Results We included 512 studies (quantitative = 426, qualitative = 71 and mixed-method = 15). Studies were conducted in 32 countries. Quantitative studies included were observational studies: cross-sectional (n = 250, 59%), longitudinal (n = 126, 30%), and case-control (n = 12, 3%); and experimental studies: pre-post design (n = 4, 1%), randomized control trial (RCT, n = 12, 3%); and not reported (n = 21, 5%). Fifteen qualitative studies did not state their study design; where stated, study design ranged from descriptive (n = 14, 20%), ethnography (n = 12, 17%), grounded theory (n = 7, 10%), narrative (n = 5, 7%), phenomenology (n = 10, 14%), interpretative exploratory (n = 4, 6%), case studies (n = 4, 6%). Of the 15 mixed-method studies, seven did not state their mixed-method design. Where stated, design includes concurrent (n = 1), convergent (n = 1), cross-sectional (n = 3), informative (n = 1), sequential exploratory (n = 1) and retrospective (n = 2). Studies were classified into 30 (for quantitative studies) and seven (for qualitative and mixed-method) subject-related areas. HIV/AIDs-related and non-communicable diseases-related studies were the most predominant subject-related areas. No studies explored the transdisciplinary co-production of interventions. Conclusions There are glaring gaps in ageing research in SSA, especially mixed-methods and RCTs. A large number of studies focused on HIV/AIDs and non-communicable disease-related studies. National and international funding agencies should set up priority funding competitions for transdisciplinary collaborations in ageing research.

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)0.055
metaresearch head score (Gemma)0.163
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.055
Threshold uncertainty score0.291

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0550.163
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0070.007
Bibliometrics0.0500.039
Science and technology studies0.0020.002
Scholarly communication0.0050.006
Open science0.0020.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.266
GPT teacher head0.428
Teacher spread0.161 · 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 designSystematic review
Domainnot available
GenreReview

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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Citations0
Published2021
Admission routes1
Has abstractyes

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