Evaluation in a nutshell: a practical guide to the evaluation of health promotion programs
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
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.
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.
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 | 0.059 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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