Response and Nonresponse Bias in Oral Health Surveys
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.
Bibliographic record
Abstract
Oral health surveys are undertaken to provide estimates of the dental health and behaviors of populations or population subgroups. However, the integrity of the data from sample surveys may be compromised by one or more sources of sampling and nonsampling error. An important source of nonsampling error is the failure to collect data from some of the individuals comprising the sample. Consequently, the response to a sample survey, and the direction and magnitude of bias induced by nonresponse, need to be taken into account when using estimates derived from sample surveys. Although the response rate to a survey is usually used as an indicator of the quality of the data it provides, nonresponse error is a function of nonresponse and the extent of differences in the characteristics of responders and nonresponders. Nonresponse may be managed in two ways. The first is to reduce nonresponse to a minimum using response-enhancement strategies. The second is the post-survey adjustment of data using weighting or imputation techniques to produce estimates that correct for nonresponse. This paper discusses issues concerning response and nonresponse bias in oral health surveys and provides guidelines on the management and reporting of nonresponse. It describes response-enhancement strategies to reduce noncontacts and refusals, sources of data to facilitate the comparison of responders and nonresponders, methods of assessing the degree of bias induced by nonresponse, techniques for producing adjusted survey estimates, and the assumptions on which these procedures and processes are based.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.498 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it