Predictors of locating women six to eight years after contact: internet resources at recruitment may help to improve response rates in longitudinal research
Bibliographic record
Abstract
BACKGROUND: The ability to locate those sampled has important implications for response rates and thus the success of survey research. The purpose of this study was to examine predictors of locating women requiring tracing using publicly available methods (primarily Internet searches), and to determine the additional benefit of vital statistics linkages. METHODS: Random samples of women aged 65-89 years residing in two regions of Ontario, Canada were selected from a list of those who completed a questionnaire between 1995 and 1997 (n = 1,500). A random sample of 507 of these women had been searched on the Internet as part of a feasibility pilot in 2001. All 1,500 women sampled were mailed a newsletter and information letter prior to recruitment by telephone in 2003 and 2004. Those with returned mail or incorrect telephone number(s) required tracing. Predictors of locating women were examined using logistic regression. RESULTS: Tracing was required for 372 (25%) of the women sampled, and of these, 181 (49%) were located. Predictors of locating women were: younger age, residing in less densely populated areas, having had a web-search completed in 2001, and listed name identified on the Internet prior to recruitment in 2003. Although vital statistics linkages to death records subsequently identified 41 subjects, these data were incomplete. CONCLUSION: Prospective studies may benefit from using Internet resources at recruitment to determine the listed names for telephone numbers thereby facilitating follow-up tracing and improving response rates. Although vital statistics linkages may help to identify deceased individuals, these may be best suited for post hoc response rate adjustment.
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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 | 0.012 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".