To send or not to send: weighing the costs and benefits of mailing an advance letter to participants before a telephone survey
Bibliographic record
Abstract
OBJECTIVE: A letter was mailed to half the participants (Letter = 137; No Letter = 138) of a 5-year follow-up survey regarding smoking cessation before attempting contact for a telephone interview. The primary outcome was the number of completed surveys per group (response rate). Secondary analyses of the number of telephone calls placed and a cost analysis were performed. RESULTS: No conclusive effect was found on the response rates per group (59.1% Letter, 50.0% No Letter; p = 0.147). Additionally, a logistic regression, controlling for demographics, revealed that there was no direct effect of sending the letter on response rate (p = 0.369). Non-parametric analysis showed significantly fewer calls (U = 7962.5, z = - 2.274, p < 0.05 two-tailed) and significantly lower costs (U = 11112.00, z = 2.521, p < 0.05 two-tailed) in reaching participants in the Letter group. Mailing an advance letter to participants did not appear to effect response rates between the groups, even when controlling for demographics. However, further analysis examining the number of call attempts and the costs per group revealed the letter may have had other effects. These findings suggest that additional analyses may be merited when evaluating the effectiveness of methods to increase participation, such as an advance letter, especially in cases where the literature largely supports its effectual use. Trial registration ClinicalTrials.gov NCT03097445. Registered 31 March 2017.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.059 | 0.201 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".