Web-Delivered Multimedia Training Materials for the Self-Collection of Dried Blood Spots: A Formative Project
Bibliographic record
Abstract
BACKGROUND: The use of dried blood spots (DBS) in biomedical research has been increasing as an objective measure for variables that are typically plagued by self-report, such as smoking status and medication adherence. The development of training materials for the self-collection of DBS that can be delivered through the Web would allow for broader use of this methodology. OBJECTIVE: The objective of this study was to evaluate the acceptability and feasibility of the self-collection of DBS using newly developed multimedia training materials that were delivered through the Web. We also aimed to assess the usability of the collected DBS samples. METHODS: We recruited participants through Facebook advertising for two distinct studies. The first study evaluated the acceptability of our newly developed DBS training materials, while the second assessed the implementation of this protocol into a larger Web-based study. RESULTS: In the first study, participants (N=115) were aged, on average, 26.1 (SD 6.4) years. Training materials were acceptable (113/115, 98.2%, of participants were willing to collect DBS again) and produced usable samples (110/115, 95.7%, collected DBS were usable). In the second study, response rate was 25.0% (41/164), with responders being significantly younger than nonresponders (20.3 [SD 0.2] vs 22.0 [SD 0.4]; P<.001), and 92% (31/41) of collected DBS samples were usable by the laboratory. CONCLUSIONS: Overall, while the protocol is acceptable, feasible, and produced usable samples, additional work is needed to improve response rates.
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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 | 0.037 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".