Using Group Concept Mapping to Engage a Hard-to-Reach Population in Research: Young Adults With Life-Limiting Conditions
Bibliographic record
Abstract
Patient engagement strategies are used in community-based participatory research. A successful strategy requires that patients, researchers, and health-care providers collaborate to create meaningful outcomes. Hard-to-reach patient populations such as those living with complex physical or psychosocial conditions, who are geographically dispersed, or who are disadvantaged financially or socially, experience judgment, stigmatization, and marginalization within society and in the research process. Therefore, strategies are needed to better engage hard-to-reach populations in research. One strategy to engage this population is group concept mapping (GCM). This article illustrates how GCM was utilized to engage a hard-to-reach population of young adults (YAs) with life-limiting conditions (LLC), parents of YAs with LLC, and health and health and community experts. Study participants were involved in generating, analyzing, and interpreting data. Five attributes of GCM are outlined, and suggestions are made for how other researchers could use GCM to engage their hard-to-reach patient populations.
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 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 | 0.079 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".