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Record W2901134544 · doi:10.1186/s13104-018-3920-6

To send or not to send: weighing the costs and benefits of mailing an advance letter to participants before a telephone survey

2018· article· en· W2901134544 on OpenAlexafffund
Christina Schell, Alexandra Godinho, Vladyslav Kushnir, John Cunningham

Bibliographic record

VenueBMC Research Notes · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSurvey Methodology and Nonresponse
Canadian institutionsUniversity of TorontoSt. Michael's HospitalCentre for Addiction and Mental Health
FundersCanada Research ChairsCanadian Cancer SocietyOntario Ministry of Health and Long-Term Care
KeywordsMedicineDemographicsLogistic regressionDemographyTelephone surveyFamily medicineAdvertisingInternal medicine

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.084
metaresearch head score (Gemma)0.138
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0840.138
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.670
GPT teacher head0.584
Teacher spread0.086 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2018
Admission routes2
Has abstractyes

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