Barriers and enablers to and strategies for pro-moting plasma donation: overarching protocol for three systematic reviews
Bibliographic record
Abstract
Introduction: The growing demand for plasma protein products has caused concern in countries such as Canada, which largely relies on importing plasma products produced fr/om plasma collected in the United States. Optimizing recruitment and retention of diverse plasma donors is therefore important for supporting national donation systems that can reliably meet the most critical needs of health services. This series of three systematic reviews aims to synthesize the known barriers and enablers to source plasma donation from the qualitative and survey-based literature and identify which strategies that have shown to be effective in promoting increased intention to, and actual donation of, source plasma.Methods and analysis: Primary studies involving source or convalescent plasma donors will be included. The search strategy will capture all potentially relevant studies to each of the three reviews, creating a database of plasma donation literature. Study designs will be subsequently identified in the screening process to facilitate analysis according to the unique inclusion criteria of each review (i.e., qualitative, survey, and experimental designs). The search will be conducted in the electronic databases SCOPUS, MEDLINE, EMBASE, PsycINFO, Google Scholar, and CINAHL without date or language restrictions. Studies will be screened, and data will be extracted, in duplicate by two independent reviewers with disagreements resolved through consensus. Reviews 1 and 2 will draw on the Theoretical Domains Framework and Intersectionality, while Review 3 will be informed by the Behaviour Change Techniques Ontologies. Directed content analysis and framework analysis (Review 1), and descriptive and inferential syntheses (Reviews 2 and 3), will be used, including meta-analyses if appropriate. Ethics and dissemination: Ethics approval is not required for literature reviews. The findings of each review will be submitted for publication and presented at a scientific conference.
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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 | 0.121 | 0.181 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.020 | 0.018 |
| Bibliometrics | 0.025 | 0.021 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.038 | 0.006 |
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".