Socio- Demographic, Clinical and Lifestyle Determinants of Low Response Rate on a Self- Reported Psychological Multi-Item Instrument Assessing the Adults’ Hostility and its Direction: ATTICA Epidemiological Study (2002-2012)
Bibliographic record
Abstract
Background: Missing data constitutes a common phenomenon, especially, in questionnaire-based, population surveys or epidemiological studies, with the statistical power, the efficiency and the validity of the conducted analyses being significantly affected by the missing information. The aim of the present work was to investigate the socio-demographic, lifestyle and clinical determinants of low response rate in a self- rating multi-item scale, estimating the individuals’ hostility and direction of hostility. Methods: 3042 apparently healthy volunteers residing in the Athens metropolitan area participated in the ATTICA epidemiological study [1514 (49.8%) were men [46 years old (SD= 13 years)] and 1528 (50.2%) were women [45 years old (SD= 14 years)]]. Hostility and Direction of Hostility was assessed with the Hostility and Direction of Hostility (HDHQ) scale. Binary logistic regression with backward model selection was used in order to identify the key demographic, clinical and lifestyle determinants of higher non-response rate in the HDHQ scale. Results: The vast majority of the participants (87.0%) had missing information in the HDHQ scale. Older age, lower educational level, poorer health status and unhealthy dietary habits, were found to be significant determinants of high nonresponse rate, while female participants were found to be more likely to have missing data in the items of the HDHQ scale. Conclusions: The present work augments prior evidence that higher non-response to health surveys is significantly affected by responders’ background characteristics, while it gives rise to research towards unrevealed paths behind this claim.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".