THE PROMISE AND CHALLENGES OF GERONTOLOGISTS WHO ADVANCE THE SCIENCE OF COMMUNITY-ENGAGED RESEARCH
Bibliographic record
Abstract
Abstract Our aging population demands more responsive policies and services and calls for a shift from involving older adults as individual participants toward collaborating with or empowering their communities as research partners. Knowing how to build capacity for community-engaged research (CEnR) among gerontologists requires understanding the challenges of such endeavors. A survey was distributed to gerontologists in March 2021 to December 2022, and was supplemented by a brainstorming focus group. We received responses from academic faculty, staff, students, and trainees (n=98) representing multiple academic disciplines from community health and implementation science to behavioral and social sciences. Many identified as a minoritized person (57.7%) and used mixed methods (55.1%). Focus group findings revealed challenges around community and participant recruitment, sustaining relationships, low-resource contexts and recognition. These reflected concerns around time, work, and funding – three most commonly-occurring words in survey responses. Barriers articulated include, “the time it takes to cultivate real relationships with community partners to design and carry out research together,” “confusion among academic colleagues on how this work is scholarship,” and a “lack of understanding and recognition from universities and funding agencies.” To more effectively mobilize knowledge and remain relevant, institutions would do well to heed calls for training on involving community members as co-researchers (78.6%), learning about rapidly developing CEnR methodologies (75.3%), and sustaining successful intervention programs after grant funding has ended (70.4%) among other priorities. This study provides critical insight into the landscape of CEnR among gerontologists and may be used to develop more supportive programs and structures.
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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 | 0.351 | 0.339 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.025 | 0.052 |
| Scholarly communication | 0.038 | 0.064 |
| Open science | 0.007 | 0.053 |
| Research integrity | 0.049 | 0.064 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".