Implications of Disagreement Between Self-Reporting and Objective Measures: A Scoping Review
Bibliographic record
Abstract
INTRODUCTION: Researchers and health specialists generally collect data and information about chronic diseases from self-reports. However, the accuracy of self-reports has been questioned as they depend on the respondents' ability to recall information and their understanding of pathological conditions. Therefore, an objective diagnosis is usually regarded as a more accurate indication of the presence of diseases.OBJECTIVE: A scoping review will examine the extent of the disagreement between self- reports and objective measures, focusing on the implications of this disagreement in terms of indicators of physical and emotional health as well provision and planning of health services.METHOD: There are few publications on the impact of disagreements between self-reporting and objective measures. In this case, a scoping review was chosen as an efficient tool to explore the issue, due to the limited amount of available evidence. This review was conducted in two major research databases: Scopus and Medline databases. The criteria of the study included all genders, age groups, and geographic areas. The source of information for the scoping review included existing literature such as guidelines, letters, meta-analyses, systematic reviews, and primary research studies.RESULT: In the 12 studies, the total participants were 155,939 and each study’s sample size ranged from 77 to 118,553. Four out of twelve studies showed a significant difference between self-reported ailments and objective diagnosis for (kappa=0.17 to 0.3), whereas the agreement was moderate for the utilization of health services and quality of ambulatory care (kappa=0.43 to 0.5), however, the agreement on whether counselling and referrals were needed was low (kappa= 0.3, 95% CI [0.3-0.3]). The disagreements between self-report and objective measures had implications regarding prevalence of diseases (20% less by self-reported) or risk factors (such as physical activity [PA]), costs of treatments (15 EUR high by reports), risk factors such as car accidents for elderly (useful field of view in elderly drivers was a risk over four times larger than obtained from self-reported [OR= 13.7 vs OR=3.4]), and utilization of health services (34.1% higher by reported).CONCLUSION: In most health domains, we found there was low to moderate disagreement between self-reporting and objective measures for diagnosing illnesses and utilization of health services. The prevalence of disease was lower when self-reported, while the utilization of health services and cost of health services were higher when self-reported than when objectively measured. This disagreement has implications regarding the increasing the cost of health services and provides a misleading basis for health planning.
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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 | 0.644 | 0.851 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.038 | 0.038 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.010 | 0.012 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".