Book Review: Mental Health Research: Health Measurement Scales: A Practical Guide to Their Development and Use. Fourth Edition
Bibliographic record
Abstract
Health Measurement Scales: A Practical Guide to Their Development and Use. Fourth Edition David L Streiner, Geoffrey R Norman. New York (NY): Oxford University Press, Ine; 2008. 43 1 p. Can$59.95 Reviewer rating: Excellent Mental Health Research Mental health researchers need to measure things (symptom severity, quality of life, and clinical competence) that are inherently difficult to measure. Fortunately, education researchers and social scientists have been grappling with many of the same issues for decades. Health Measurement Scales: A Practical Guide to Their Development and Use by David Streiner and Geoffrey Norman brings much of this experience with measurement research into focus for health researchers. The emphasis of this book is on health measurement scales - not specifically on those concerned with mental health. However, almost all of the content in this book is relevant to measurement in psychiatric research. The target audience is health researchers, broadly defined. The book has been prepared primarily as a practical guide to the development of rating scales. The title also refers to the use of health measurement scales. The information contained within it will be of assistance for readers who need to evaluate literature supporting various measurement strategies and to use measurement scales in research projects. There is no intention to cover issues related to the use of measurement scales in clinical practice. Consistent with this, the book is organized to approximately follow the process of developing a new instrument from its very beginning (is a new scale needed at all?) to topics such as item development and selection, core topics such as reliability and validity, methods of administration, research ethics and standards for publishing studies that assess measurements scales (for example, Standards for Reporting of Diagnostic Accuracy, STARD). The book is not a compendium of scales, nor does it contain detailed reviews or recommendations about specific instruments. The focus is mostly on the conceptual and methodological underpinnings of health measurement and related consequences for scale development. The book is rich with computational equations but it does not delve deeply into their mathematical basis (an appendix with further reading is provided). This allows the book to maintain a middle ground, avoiding both the inaccessibility of much of the methodologically oriented psychometric literature and the naivety of the how-to chapters in software manuals and related sources. This is not to say that the book does not provide practical tools. …
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.110 | 0.126 |
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".