Using theory of change to design and evaluate public health interventions: a systematic review
Bibliographic record
Abstract
BACKGROUND: Despite the increasing popularity of the theory of change (ToC) approach, little is known about the extent to which ToC has been used in the design and evaluation of public health interventions. This review aims to determine how ToCs have been developed and used in the development and evaluation of public health interventions globally. METHODS: We searched for papers reporting the use of "theory of change" in the development or evaluation of public health interventions in databases of peer-reviewed journal articles such as Scopus, Pubmed, PsychInfo, grey literature databases, Google and websites of development funders. We included papers of any date, language or study design. Both abstracts and full text papers were double screened. Data were extracted and narratively and quantitatively summarised. RESULTS: A total of 62 papers were included in the review. Forty-nine (79 %) described the development of ToC, 18 (29 %) described the use of ToC in the development of the intervention and 49 (79 %) described the use of ToC in the evaluation of the intervention. Although a large number of papers were included in the review, their descriptions of the ToC development and use in intervention design and evaluation lacked detail. CONCLUSIONS: The use of the ToC approach is widespread in the public health literature. Clear reporting of the ToC process and outputs is important to strengthen the body of literature on practical application of ToC in order to develop our understanding of the benefits and advantages of using ToC. We also propose a checklist for reporting on the use of ToC to ensure transparent reporting and recommend that our checklist is used and refined by authors reporting the ToC approach.
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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 | 0.216 | 0.412 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.018 |
| Bibliometrics | 0.028 | 0.024 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".