MétaCan
Menu
Back to cohort
Record W2114802681 · doi:10.1186/1472-6963-12-250

Do incentives, reminders or reduced burden improve healthcare professional response rates in postal questionnaires? two randomised controlled trials

2012· article· en· W2114802681 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBMC Health Services Research · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicSurvey Methodology and Nonresponse
Canadian institutionsUniversity of Ottawa
FundersMedical Research CouncilChief Scientist Office, Scottish Government Health and Social Care DirectorateScottish GovernmentWellcome Trust
KeywordsMedicineRandomized controlled trialPsychological interventionIncentiveNursing researchPhysical therapyFamily medicineHealth careNursingSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Healthcare professional response rates to postal questionnaires are declining and this may threaten the validity and generalisability of their findings. Methods to improve response rates do incur costs (resources) and increase the cost of research projects. The aim of these randomised controlled trials (RCTs) was to assess whether 1) incentives, 2) type of reminder and/or 3) reduced response burden improve response rates; and to assess the cost implications of such additional effective interventions. METHODS: Two RCTs were conducted. In RCT A general dental practitioners (dentists) in Scotland were randomised to receive either an incentive; an abridged questionnaire or a full length questionnaire. In RCT B non-responders to a postal questionnaire sent to general medical practitioners (GPs) in the UK were firstly randomised to receive a second full length questionnaire as a reminder or a postcard reminder. Continued non-responders from RCT B were then randomised within their first randomisation to receive a third full length or an abridged questionnaire reminder. The cost-effectiveness of interventions that effectively increased response rates was assessed as a secondary outcome. RESULTS: There was no evidence that an incentive (52% versus 43%, Risk Difference (RD) -8.8 (95%CI -22.5, 4.8); or abridged questionnaire (46% versus 43%, RD -2.9 (95%CI -16.5, 10.7); statistically significantly improved dentist response rates compared to a full length questionnaire in RCT A. In RCT B there was no evidence that a full questionnaire reminder statistically significantly improved response rates compared to a postcard reminder (10.4% versus 7.3%, RD 3 (95%CI -0.1, 6.8). At a second reminder stage, GPs sent the abridged questionnaire responded more often (14.8% versus 7.2%, RD -7.7 (95%CI -12.8, -2.6). GPs who received a postcard reminder followed by an abridged questionnaire were most likely to respond (19.8% versus 6.3%, RD 8.1%, and 9.1% for full/postcard/full, three full or full/full/abridged questionnaire respectively). An abridged questionnaire containing fewer questions following a postcard reminder was the only cost-effective strategy for increasing the response rate (£15.99 per response). CONCLUSIONS: When expecting or facing a low response rate to postal questionnaires, researchers should carefully identify the most efficient way to boost their response rate. In these studies, an abridged questionnaire containing fewer questions following a postcard reminder was the only cost-effective strategy. An increase in response rates may be explained by a combination of the number and type of contacts. Increasing the sampling frame may be more cost-effective than interventions to prompt non-responders. However, this may not strengthen the validity and generalisability of the survey findings and affect the representativeness of the sample.

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.

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)0.574
metaresearch head score (Gemma)0.126
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.710
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.5740.126
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.365
GPT teacher head0.609
Teacher spread0.244 · 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