Bibliographic record
Abstract
Abstract One effect of rising health care costs has been to raise the profile of studies that evaluate care and create a systematic evidence base for therapies and, by extension, for health policies. All clinical trials and evaluative studies require instruments to monitor the outcomes of care in terms of quality of life, disability, pain, mental health, or general well-being. Many measurement tools have been developed, and choosing among them is difficult. This book provides comparative reviews of the quality of leading health measurement instruments and a technical and historical introduction to the field of health measurement, and discusses future directions in the field. This edition reviews over 100 scales, presented in chapters covering physical disability, psychological well-being, anxiety, depression, mental status testing, social health, pain measurement, and quality of life. An introductory chapter describes the theoretical and methodological development of health measures, while a final chapter reviews the current status of the field, indicating areas in which further development is required. Each chapter includes a tabular comparison of the quality of the instruments reviewed, followed by a detailed description of each instrument, covering its purpose and conceptual basis, its reliability and validity, alternative versions and, where possible, a copy of the scale itself. To ensure accuracy, each review has been approved by the original author of each instrument or by an acknowledged expert.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.009 |
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".