Bibliographic record
Abstract
This book is designed for nonmedical prescribers, including pharmacists, nurses, and optometrists.The book is intended as a guide for health care professionals who are interested in becoming supplementary or independent prescribers in the United Kingdom or as a resource for those who are already prescribers.The book has 11 chapters divided into 2 sections.The first part of the book covers the key areas to consider for prescribing safely and effectively.The second part provides a brief overview of clinical topics that are of relevance for the nonmedical prescriber.Key learning points are presented at the beginning of each chapter, which is a nice feature and helps to orient the reader to the chapter's content.The chapters are nicely laid out, with subheadings appropriate to the content.Each of the clinical chapters is written by a practitioner in the area and provides clinical insights and reflections from prescribers, as well as case studies.The first few chapters focus on the legal and ethical aspects of nonmedical prescribing in the United Kingdom, as well as clinical governance.Much detail is presented about the UK legislation, as well as the clinical governance framework established by the UK's National Health Service for continuous quality improvement in health care.These first few chapters may have little relevance to pharmacists in Canada.Chapter 4 provides an overview of the principles of prescribing and of designing dosage regimens.There is also a brief review of adverse drug reactions, drug interactions, and pharmacokinetic principles.These sections are not very comprehensive, and the reader will need to refer to other resources for more detailed information.Chapter 5 is the most useful chapter of the book and is relevant to a more global audience.It focuses on the principles of clinical decision-making and evidence-based prescribing.The chapter ends with some helpful hints on errors and pitfalls to avoid in prescribing.Chapters 6 to 11 discuss prescribing for specific clinical topics: diabetes mellitus, cardiology, respiratory diseases, palliative care, oncology, and mental health.Each chapter begins with the principles of prescribing for that area, followed by a summary of the knowledge, competencies, and skills required for prescribing.Aspiring prescribers could use these lists as self-assessment checklists to identify their learning needs.Each chapter ends with a series of four or five case studies.Most of the case studies are structured in question-and-answer format, but this approach is not consistent for all chapters, nor is the same format of questioning used for all case studies.Readers may find this inconsistency frustrating.Furthermore, the case scenarios are
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.102 | 0.015 |
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".