Evidence-based practice: a primer for health care professionals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 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 | 0.014 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it