Research priority setting related to older adults: a scoping review to inform the Cochrane-Campbell Global Ageing Partnership work programme
Bibliographic record
Abstract
OBJECTIVE: To explore and map the findings of prior research priority-setting initiatives related to improving the health and well-being of older adults. DESIGN: Scoping review. DATA SOURCES: Searched MEDLINE, EMBASE, AgeLine, CINAHL and PsycINFO databases from January 2014 to 26 April 2021, and the James Lind Alliance top 10 priorities. ELIGIBILITY CRITERIA: We included primary studies reporting research priorities gathered from stakeholders that focused on ageing or the health of older adults (≥60 years). There were no restrictions by setting, but language was limited to English and French. DATA EXTRACTION AND SYNTHESIS: We used a modified Reporting Guideline for Priority Setting of Health Research (REPRISE) guideline to assess the transparency of the reported methods. Population-intervention-control-outcome (PICO) priorities were categorised according to their associated International Classification of Health Interventions (ICHI) and International Classification of Functioning (ICF) outcomes. Broad research topics were categorised thematically. RESULTS: Sixty-four studies met our inclusion criteria. The studies gathered opinions from various stakeholder groups, including clinicians (n=56 studies) and older adults (n=35), and caregivers (n=24), with 75% of the initiatives involving multiple groups. None of the included priority-setting initiatives reported gathering opinions from stakeholders located in low-income or middle-income countries. Of the priorities extracted, 272 were identified as broad research topics, while 217 were identified as PICO priorities. PICO priorities that involved clinical outcomes (n=165 priorities) and interventions concerning health-related behaviours (n=59) were identified most often. Broad research topics on health services and systems were identified most often (n=60). Across all these included studies, the reporting of six REPRISE elements was deemed to be critically low. CONCLUSION: Future priority setting initiatives should focus on documenting a more detailed methodology with all initiatives eliciting opinions from caregivers and older adults to ensure priorities reflect the opinions of all key stakeholder groups.
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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 | 0.166 | 0.451 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.016 | 0.013 |
| Bibliometrics | 0.079 | 0.062 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".