Bibliographic record
Abstract
Abstract This is the new edition of the highly successful practical guide for clinicians developing tools to measure subjective states, attitudes or non-tangible outcomes in their patients. It is widely used by people from many disciplines, who have only a limited knowledge of statistics. This thoroughly updated edition of Health Measurement Scales, Third Edition gives more details on cognitive requirements in answering questions, and how this influences scale development. There is now an expanded discussion of generalizability theory, a completely revised chapter on Item Response Theory and many revisions are included, based on the latest research findings. These features combine to provide the most up-to-date guide to measuring scale development available. It synthesizes the theory of scale construction with practical advice, culled from the literature and the authors’ experience, about how to develop and validate measurement scales to be used in the health sciences. The theory goes into issues of reliability, generalizability theory, validity, the measurement of change, the cognitive requirements of answering questions, and item response theory. Practical issues cover devising the items, biases that may affect the responses, pre-testing and weeding out poorly performing items, combining items into scales, setting cut points, and the practical issues of using scales in various ways, such as face-to-face interviews; mailed or telephone-administered surveys; and over the internet. One chapter also discusses some of the ethical issues that scale developers and users should be aware of. Appendices lead the reader to other readings; sources of already developed scales and items; and a very brief introduction to exploratory and confirmatory factor analysis.
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 | 0.013 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.074 | 0.028 |
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".