Recommendations for reporting the results of studies of instrument and scale development and testing
Bibliographic record
Abstract
Scales and instruments play an important role in health research and practice. It is important that studies that report on their psychometric properties do so in a way such that readers can understand what was done and what was found. This paper is a guide to writing articles about the development and assessment of these tools. It covers what should be in the abstract and how key words should be chosen. The article then discusses what should be in the main parts of the paper: the introduction, methods, results and discussion. In each of these parts, it suggests the statistical tests that should be used and how to report them. The emphasis throughout the paper is that reliability and validity are not fixed properties of a scale, but depend on an interaction among it, the population being evaluated and the circumstances under which the instrument is administered.
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 | 0.650 | 0.921 |
| Meta-epidemiology (narrow) | 0.006 | 0.010 |
| Meta-epidemiology (broad) | 0.012 | 0.019 |
| Bibliometrics | 0.041 | 0.047 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.028 | 0.034 |
| Open science | 0.018 | 0.014 |
| Research integrity | 0.023 | 0.039 |
| Insufficient payload (model declined to judge) | 0.017 | 0.020 |
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".