A review of reported network degree and recruitment characteristics in respondent driven sampling implications for applied researchers and methodologists
Bibliographic record
Abstract
OBJECTIVE: Respondent driven sampling (RDS) is an important tool for measuring disease prevalence in populations with no sampling frame. We aim to describe key properties of these samples to guide those using this method and to inform methodological research. METHODS: In 2019, authors who published respondent driven sampling studies were contacted with a request to share reported degree and network information. Of 59 author groups identified, 15 (25%) agreed to share data, representing 53 distinct study samples containing 36,547 participants across 12 countries and several target populations including migrants, sex workers and men who have sex with men. Distribution of reported network degree was described for each sample and characteristics of recruitment chains, and their relationship to coupons, were reported. RESULTS: Reported network degree is severely skewed and is best represented by a log normal distribution. For participants connected to more than 15 other people, reported degree is imprecise and frequently rounded to the nearest five or ten. Our results indicate that many samples contain highly connected individuals, who may be connected to at least 1000 other people. CONCLUSION: Because very large reported degrees are common; we caution against treating these reports as outliers. The imprecise and skewed distribution of the reported degree should be incorporated into future RDS methodological studies to better capture real-world performance. Previous results indicating poor performance of regression estimators using RDS weights may be widely generalizable. Fewer recruitment coupons may be associated with longer recruitment chains.
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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 | 0.312 | 0.567 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.024 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".