Quality of Survey Reporting in Nephrology Journals
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Survey research is an important research method used to determine individuals' attitudes, knowledge, and behaviors; however, as with other research methods, inadequate reporting threatens the validity of results. This study aimed to describe the quality of reporting of surveys published between 2001 and 2011 in the field of nephrology. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: The top nephrology journals were systematically reviewed (2001-2011: American Journal of Kidney Diseases, Nephrology Dialysis Transplantation, and Kidney International; 2006-2011: Clinical Journal of the American Society of Nephrology) for studies whose primary objective was to collect and report survey results. Included were nephrology journals with a heavy focus on clinical research and high impact factors. All titles and abstracts were screened in duplicate. Surveys were excluded if they were part of a multimethod study, evaluated only psychometric characteristics, or used semi-structured interviews. Information was collected on survey and respondent characteristics, questionnaire development (e.g., pilot testing), psychometric characteristics (e.g., validity and reliability), survey methods used to optimize response rate (e.g., system of multiple contacts), and response rate. RESULTS: After a screening of 19,970 citations, 216 full-text articles were reviewed and 102 surveys were included. Approximately 85% of studies reported a response rate. Almost half of studies (46%) discussed how they developed their questionnaire and only a quarter of studies (28%) mentioned the validity or reliability of the questionnaire. The only characteristic that improved over the years was the proportion of articles reporting missing data (2001-2004: 46.4%; 2005-2008: 61.9%; and 2009-2011: 84.8%; respectively) (P<0.01). CONCLUSIONS: The quality of survey reporting in nephrology journals remains suboptimal. In particular, reporting of the validity and reliability of the questionnaire must be improved. Guidelines to improve survey reporting and increase transparency are clearly needed.
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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 | 0.326 | 0.663 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.023 | 0.030 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| 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; 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".