Patient voices to enhance concussion research participation: an exploratory qualitative study
Bibliographic record
Abstract
PURPOSE: There are unique characteristics of vulnerability among adult patients who experience a concussion and have persistent symptoms suggesting the need to tailor recruitment and retention strategies for this population. We aimed to obtain perspectives from post-concussion patients regarding factors they value to encourage recruitment and support retention, thereby assisting research teams conducting studies on concussion. METHODS: The authors used purposive, nonrandom sampling to identify potential research participants for this exploratory qualitative sub-study. Interview questions were designed using Appreciative Inquiry to gain patient-centered approaches to support recruitment and retention. Transcripts from telephone interviews were analyzed using reflexive thematic analysis. RESULTS: We identified two main themes: 1) positive change and 2) participant-centered study design. Each main theme had three sub-themes to support recruitment and/or retention. Positive changes included a) meaningful study impact, b) personal contribution counts, and c) gain information, and reassurance. Participant-centered study design included a) convenience, b) accommodation, and c) feeling valued. CONCLUSIONS: Focused efforts in planning to recruit and retain vulnerable populations for research are paramount. Based on our exploratory findings from a limited sample, we offer patient-reported insights to reflect upon the ongoing learning process to optimize recruitment and retention in the field of concussion research.
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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 | 0.043 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".