Motivation to participate and attrition factors in a COVID-19 biobank: A qualitative study
Bibliographic record
Abstract
BACKGROUND: The Biobanque québécoise de la COVID-19 (Quebec Biobank for COVID-19, or BQC19) is a provincial initiative that aims to manage the longitudinal collection, storage, and sharing of biological samples and clinical data related to COVID-19. During the study, BQC19 investigators reported a high loss-to-follow-up rate. The current study aimed to explore motivational and attrition factors from the perspective of BQC19 participants and health care and research professionals. METHODS: This was an inductive exploratory qualitative study. Using a theoretical sampling approach, a sample of BQC19 participants and professionals were invited to participate via semi-structured interviews. Topics included motivations to participate; participants' fears, doubts, and barriers to participation; and professionals' experiences with biobanking during the COVID-19 pandemic. RESULTS: Interviews were conducted with BQC19 participants (n = 23) and professionals (n = 17) from 8 clinical data collection sites. Motivations included the contribution to science and society in crisis, self-worth, and interactions with medical professionals. Reasons for attrition included logistical barriers, negative attitudes about public health measures or genomic studies, fear of clinical settings, and a desire to move on from COVID-19. Motivations and barriers seemed to evolve over time and with COVID-19 trends and surges. Certain situations were associated with attrition, such as when patients experienced indirect verbal consent during hospitalization. Barriers related to human and material resources and containment/prevention measures limited the ability of research teams to recruit and retain participants, especially in the ever-evolving context of crisis. CONCLUSION: The pandemic setting impacted participation and attrition, either by influencing participants' motivations and barriers or by affecting research teams' ability to recruit and retain participants. Longitudinal and/or biobanking studies in a public health crisis setting should consider these factors to limit attrition.
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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 | 0.030 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".