The reassurance questionnaire: Comparison of the latent structure in university, community, and medical samples
Bibliographic record
Abstract
The Reassurance Questionnaire (RQ; Speckens, Spinhoven, Van Hemert, & Bolk, (2000 Speckens, A. E.M., Spinhoven, P., Van Hemert, A. M. and Bolk, J. H. 2000. The reassurance questionnaire (RQ): Psychometric properties of a self-report questionnaire to assess reassurability. Psychological Medicine, 30: 841–847. [Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) is a self-report measure designed to assess the extent to which patients feel reassured by their attending physicians. While the original RQ was validated in Dutch, the invariance of the factor structure has not been examined in the English version of the RQ. In the current study, the English RQ was completed by university (n = 459), community (n = 244), and medical samples (n = 281). Unlike the original one-factor solution found for the Dutch RQ, a two-factor solution for the English RQ was found for all three samples. The two factors were labeled: (1) Doubt in Physician, and (2) Persistent Health Anxiety. Item loadings were invariant across the community and medical samples. Implications of the findings along with directions for future research are discussed.
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.
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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".