Understanding heterogeneity and building capacity for research in long-term care and geriatric settings: a systematic qualitative review and conceptual mapping framework using mixed methods
Bibliographic record
Abstract
Abstract Background Older adults are underrepresented in research. Heterogeneity of research processes in this population, specifically in long-term care (LTC) and geriatric acute care (GAC), is not well described and may impede the design, planning, and conduct of research. Objective Identify, quantify, and map stakeholders, research stages, and transversal themes of research processes, to develop a mapping framework. Methods Multicomponent mixed methods study. An environmental scan was used to initiate a preliminary framework. We conducted a systematic literature search on process, barriers, and methods for clinical research in GAC and LTC to extract and update stakeholders, research stages, and themes. Importance and interactions of elements were synthesized via heatmaps by number of articles, mentions, and content intersections. Results For our initial framework, we surveyed 24 stakeholders. Of 9277 records, 68 articles were included with 12 stakeholders, 13 research stages, 17 transversal themes (either barriers, facilitators, general themes, or recommendations), and 1868 intersections. Differences in relative importance between LTC and GAC emerged for stakeholders (staff, managers vs. caregivers, ethics committees), and for research stages (funding, facility recruitment vs. ethics, individual recruitment). Crucial themes by stakeholders were collaboration for the research team; communication, trust, and human resources for managers; heterogeneity for patients and residents. A heatmap framework synthesizing vital stakeholders and themes per research stage was generated. Conclusions We identified and quantified the interactions between stakeholders, stages, and themes to characterize heterogeneity in LTC and GAC research. Our framework may serve as a blueprint to co-construct and improve each stage of the research process.
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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 | 0.318 | 0.314 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.032 | 0.027 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".