How Semantics Connotations May Influence Concerns About Donation of Biospecimens
Bibliographic record
Abstract
Introduction: Human biological specimen (biospecimen) donation is routinely requested for clinical care and research purposes. Successfully engaging patients and research participants in biospecimen donation depends on what they understand these initiatives entail, including their perceptions of risk. Human biospecimens are stored in facilities routinely referenced as biobanks or biorepositories, both of which labels are known to embody a variety of connotations. The words chosen to describe biospecimen facilities may influence decisions about donation. Objective: To explore differences in likelihood of donation as a function of the words chosen to represent human biospecimen storage facilities and the commensurate concerns each label evokes. Materials and Methods: Two-group experimental design. Participants completed a survey about a fictitious undertaking requesting that they consider biospecimen donation. The term used to describe the facility housing the biospecimens differed; one half of the surveys referenced a biobank, and one half referenced a biorepository. Results: Two thousand five hundred ninety-six surveys were distributed; 586 completed surveys were received (response rate: 22.6%). Sixty-three percent of respondents, regardless of whether the label referenced a biobank or biorespository, reported being extremely likely to donate. There were no significant differences between the 2 groups on the 11 concerns sampled. Factor analyses revealed that concerns could be classified in two groups: use-related concerns and person-related concerns. The label biobank evoked significantly lower perception of importance of the person-related concerns sampled (e.g., personal or other benefit, discomfort or inconvenience). Conclusions: Our results suggest that researchers may consider using the word biobank to describe the facility housing the biospecimen, as this term appears less subject to concern biases. These outcomes confirm that misunderstandings or misattributions of words used to refer to biospecimen facilities could deter participation in clinical care or research. Participation may be enhanced through ensuring clear understanding of what biospecimen donation entails and by directly addressing common semantic misunderstandings and associations.
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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 | 0.007 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".