Evaluation in a nutshell: a practical guide to the evaluation of health promotion programs
Bibliographic record
Abstract
Evaluation in a Nutshell 2 is a succinct guide to strategic and technical issues in evaluation of health promotion programs. You will be given valuable advice on planning and accountability in health promotion.<br/><br/>Adrian Bauman and Don Nutbeam, both professors and professional in the Health Promotion field have written this Evaluation in a Nutshell 2 with passion and content to assist other Health Promotion professionals wanting to make a difference in to our public's health.<br/><br/>Key features in this edition include an Online learning center to contain examples of each of the styles of evaluation and examples of research design which will be updated annually, each chapter will be reviewed and revised (five independent reviews commissioned by lecturers in public health promotion, including a reviewer from the University of Montreal), new case studies, examples and references will be used, the science of ‘dissemination research’ has evolved and the authors will compare their model of dissemination with the US standard (the REAIM framework) to ensure currency, the different components of research that can contribute to program evaluation will be further explained and new designs and methods for understanding how interventions work, there will also be a short new section on policy research and its role in program evaluation as well as the economic appraisal of programs.<br/><br/>Also new to this edition will be an Online Learning Centre the authors will write case studies to give examples of the styles of evaluation, examples of research design and examples of measurement designed for use by any reader of the text, student or professional.
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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 | 0.070 | 0.126 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.058 | 0.048 |
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".