Can biosampling really be “non-invasive”? An examination of the socially invasive nature of physically non-invasive biosampling in urban and rural Malawi
Bibliographic record
Abstract
Glucocorticoids are understood to represent useful biomarkers of stress and can be measured in saliva, hair, and breastmilk. The collection of such biosamples is increasingly included in biobank and cohort studies. While collection is considered “non-invasive” by biomedical researchers (compared to sampling blood), community perspectives may differ. This cross-sectional, qualitative study utilising eight focus groups aimed to determine the feasibility and acceptability of collecting ostensibly “non-invasive” biological samples in Malawi. Breastfeeding women, couples, field workers, and healthcare providers were purposively sampled. Data about prior understandings of, barriers to, and feasibility of “non-invasive” biosampling were analysed. Participants described biomaterials intended for “non-invasive” collection as sometimes highly sensitive, with sampling procedures raising community concerns. Sampling methods framed as physically “non-invasive” within biomedicine can consequently be considered socially “invasive” by prospective sample donors. Biomedical and community framings of “invasiveness’ can therefore diverge, and the former must respond to and be informed by the perspectives of the latter. Further, considerations of collection procedures are shaped by therapeutic misconceptions about the immediate health-related utility of biomedical and public health research. When researchers engage with communities about biosampling, they must ensure they are not furthering therapeutic misconceptions and actively seek to dispel these.
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 | 0.051 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.027 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".