Research staff’s experiences of how the COVID-19 pandemic impacted recruitment for a paediatric network study
Bibliographic record
Abstract
PURPOSE: Since the COVID-19 pandemic, a paediatric network study with clinical sites across Canada suffered a reduction in participation. When research studies fail to meet enrolment targets, it can reduce the strength and validity of the results. This study explores research staff's experiences of how the COVID-19 pandemic impacted recruitment for a paediatric network study. METHODS: This study was conducted using a qualitative design. Focus group sessions were used to gain the perspective of research staff involved in recruitment and transcripts were analysed using Colaizzi's seven-step method of data analysis. RESULTS: Analysis revealed four major themes: (1) the COVID-19 pandemic had an impact on research activity; (2) families of children with medical complexity perform a risk-benefit assessment when deciding whether to take part in research; (3) a trusting relationship with clinicians is a key factor in research recruitment; and (4) research needs to be flexible in order to adapt to evolving contexts. CONCLUSION: This study identified both COVID-19 and non-COVID-19-related factors that impacted study recruitment for a paediatric network study. Understanding and addressing these challenges will mitigate the negative impacts on health outcomes that can occur when research studies fail to meet enrolment targets.
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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 | 0.103 | 0.162 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.015 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".