Book Review: Mental Health Research, Choosing Methods in Mental Health Research: Mental Health Research from Theory to Practice
Bibliographic record
Abstract
Choosing Methods in Mental Health Research: Mental Health from Theory to Practice Mike Slade and Stefan Priebe, editors. New York (NY): Routledge, 2006. 298 p. US$53.95. Reviewer rating: Very good Review by David L Streiner, PhD, C Psych Toronto, Ontario It is unusual for a book's title to understate its scope and to potentially limit its intended audience. However, Choosing Methods in Mental Health does not do justice to the contents of this book. Of the 21 chapters, only 7 are devoted to research methods. The uniqueness of the book is found in the 9 chapters in the second section, called Consumers of Research, and the final 4 chapters in the Generating High-Impact Research section. The first chapter of the first section, written by the editors, sets the stage for the rest of the book; Who Is For? makes the case that the traditional model of researchers writing scholarly articles to be read only by other researchers, can (and often does) lead to misinterpretation of the findings by the public and by policy-makers. It argues that researchers need to come to grips with the fact that experts are no longer the sole purveyors of research findings (if they ever were) and that the rise of consumerism has led people to rely on other sources of information, which may be fallacious. They use the example of the measles, mumps, and rubella (MMR) vaccine; despite very strong evidence, based on more than 2 million children, that there is no link between MMR and autism a case-series of 12 children claiming such a link was sufficient to result in widespread opposition to vaccination by parents. The next 7 chapters cover a variety of research methods used in mental health research, moving from the studies of individuals to populations: single-case experimental designs, conversation analysis, discourse analysis, grounded theory, randomized controlled trials, systematic reviews and metaanalysis, and surveys. Each chapter follows a common format: a brief description of the method, assumptions and theoretical framework, strengths and limitations of the method, the types of questions that can be answered with the technique, a brief example, and finally how the method can be used in future mental health research. Although each of the authors is an expert in their field, the chapters are balanced and fair, and avoid the implication that the method being described is the only road to truth. The second section addresses the issue of how research can influence consumers of research. As with the first section, it begins with the individual (primary care physicians), and moves on to community mental health teams, the public perception of mental illness, the media, and finally governmental policy. These chapters also follow a common format: the types of evidence that have salience, an example of a study that was successful in changing practice or policy, as well as what does not work with an example. …
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.477 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads 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".