Challenges to evaluating complex interventions: a content analysis of published papers
Bibliographic record
Abstract
BACKGROUND: There is continuing interest among practitioners, policymakers and researchers in the evaluation of complex interventions stemming from the need to further develop the evidence base on the effectiveness of healthcare and public health interventions, and an awareness that evaluation becomes more challenging if interventions are complex.We undertook an analysis of published journal articles in order to identify aspects of complexity described by writers, the fields in which complex interventions are being evaluated and the challenges experienced in design, implementation and evaluation. This paper outlines the findings of this documentary analysis. METHODS: The PubMed electronic database was searched for the ten year period, January 2002 to December 2011, using the term "complex intervention*" in the title and/or abstract of a paper. We extracted text from papers to a table and carried out a thematic analysis to identify authors' descriptions of challenges faced in developing, implementing and evaluating complex interventions. RESULTS: The search resulted in a sample of 221 papers of which full text of 216 was obtained and 207 were included in the analysis. The 207 papers broadly cover clinical, public health and methodological topics. Challenges described included the content and standardisation of interventions, the impact of the people involved (staff and patients), the organisational context of implementation, the development of outcome measures, and evaluation. CONCLUSIONS: Our analysis of these papers suggests that more detailed reporting of information on outcomes, context and intervention is required for complex interventions. Future revisions to reporting guidelines for both primary and secondary research may need to take aspects of complexity into account to enhance their value to both researchers and users of research.
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.
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 | 0.283 | 0.624 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.119 | 0.106 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".