Bibliographic record
Abstract
The readers are informed in the foreword of Evidence-Based Practice: a primer for health care professionals that this is just the beginning of their lifelong quest to understand and apply evidence-based principles to practice and, in some cases, to research.The book comprises 20 chapters by 6 authors from various walks of academic life (primary care, general practice, social sciences, health economics, nursing, and library and information science).The scope of evidence-based medicine is defined in the first chapter, and the types of questions one might seek answers for are subsequently explored.The authors then describe such information sources as web sites, databases, and evidence-based journals that can be used to answer these questions.In subsequent chapters, randomised controlled trials, studies of diagnostic tests, case series, case reports, case control studies, and systematic reviews are discussed.The chapter on economic analysis is the best summary of this potentially confusing topic that I have ever read.And it was a relief to see that qualitative research made an appearance in this book.After the chapter on critical appraisal of clinical practice guidelines (which is one of the shortest on record-just enough to introduce readers to one set of criteria for appraisal), the remaining chapters use practical examples to develop the principles introduced by earlier chapters.For example, the effectiveness of diagnostic tests and therapies are discussed and cost effectiveness is again explained but all in more depth.The final section is appropriately directed to methods for implementing research findings and disseminating critical appraisal and evidence-based medicine.Evidence-Based Practice: a primer for health care professionals encapsulates most of the knowledge and some of the skills necessary to practice and to teach evidence-based medicine, and it is written specifically for those of us in primary care.On the downside, some European terminology is used that could be confusing to North American readers, and the writing sometimes borders on the political or the evangelical (the latter tendency being common for evidence-based medicine enthusiasts!).Because the book originated as a compilation of educational material for the Oxford Master's level course in evidence-based health care, it is a ready reference to keep on one's shelf and does not necessarily need to be read cover to cover, chapter by chapter.As a family practitioner and researcher, I found the text to be an excellent resource.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.051 | 0.076 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.025 | 0.035 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.021 | 0.054 |
| Insufficient payload (model declined to judge) | 0.013 | 0.013 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".