Making integration foundational in population health intervention research: why we need ‘Work Package Zero’
Bibliographic record
Abstract
OBJECTIVES: We aimed to identify when and how integration should take place within evaluations of complex population health interventions (PHIs). STUDY DESIGN: Descriptive analytical approach. METHODS: We draw on conceptual insights that emerged through (1) a working group on integration and (2) a diverse range of literature on case studies, small-n evaluations and mixed methods evaluation studies. RESULTS: We initially sought techniques to integrate analyses at the end of a complex PHI evaluation. However, this conceptualization of integration proved limiting. Instead, we found value in conceptualizing integration as a process that commences at the beginning of an evaluation and continues throughout. Many methods can be used for this type of integration, including process tracing, realist evaluation, congruence analysis, general elimination methodology/modus operandi, pattern matching and contribution analysis. Clearly signposting when integrative methods should commence within an evaluation should be of value to the PHI evaluation community, as well as to funders and related stakeholders. CONCLUSIONS: Rather than being a tool used at the end of an evaluation, we propose that integration is more usefully conceived as a process that commences at the start of an evaluation and continues throughout. To emphasize the importance of this timing, integration can be described as comprising 'Work Package Zero' within evaluations of complex PHIs.
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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 | 0.560 | 0.589 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.011 | 0.053 |
| Scholarly communication | 0.027 | 0.060 |
| Open science | 0.007 | 0.037 |
| Research integrity | 0.012 | 0.019 |
| 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".