Building consensus on priority areas for Sub-Saharan Africa’s ageing population research: An e-Delphi study protocol
Bibliographic record
Abstract
BACKGROUND: Improvement in medico-social services has increased life expectancy and population ageing in Sub-Saharan Africa (SSA). It was estimated that about 163 million people aged 65 and older will be resident in SSA by 2050. There is inadequate ageing research capacity in SSA which necessitates this study to (a) identify a decade-long ageing research opportunities, challenges, and solutions, and (b) prioritize critical ageing research areas and methodologies relevant to the SSA. METHODS: We designed an e-Delphi protocol following the Reporting Guideline for Priority Setting of Health Research with Stakeholder. The stakeholders will be researchers, practitioners, older adults, and caregivers purposively selected through snowballing quota sampling to complete three rounds of e-Delphi surveys. Round 1 will involve open-ended questions derived from the study objectives. Responses from round 1 will be prepared as a checklist for stakeholders to rate during rounds 2 & 3, using a 9-point scale: low priority (1-3), moderate priority (4-6), and high priority (7-9). The criterion for reaching a consensus will be ≥ 70% of stakeholders rating an item "high priority" and ≤ 15% as "low priority." Quantitative data will be analysed using descriptive statistics, Wilcoxon matched-pairs signed-rank test will be used to assess the stability of stakeholders' responses, and qualitative comments will be analysed using content analysis. DISCUSSION AND IMPLICATIONS: Setting aging research/practice priorities will help maximize the benefits of research investment and provide valuable direction for allocating public and private research funds to areas of strategic importance.
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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 | 0.174 | 0.115 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.040 | 0.010 |
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".