Identification of Complex Health Interventions Suitable for Evaluation: Development and Validation of the 8-Step Scoping Framework
Bibliographic record
Abstract
BACKGROUND: There is extensive literature on the methodology of evaluation research and the development and evaluation of complex interventions but little guidance on the formative stages before evaluation and how to work with partner organizations that wish to have their provision evaluated. It is important to be able to identify suitable projects for evaluation from a range of provision and describe the steps required, often with academic institutions working in partnership with external organizations, in order to set up an evaluation. However, research evaluating programs or interventions rarely discusses these stages. OBJECTIVE: This study aimed to extend work on evaluability assessment and pre-evaluation planning by proposing an 8-Step Scoping Framework to enable the appraisal of multiple programs in order to identify interventions suitable for evaluation. We aimed to add to the literature on evaluability assessment and more recent evaluation guidance by describing the processes involved in working with partner organizations. METHODS: This paper documents the steps required to identify multiple complex interventions suitable for process and outcome evaluation. The steps were developed using an iterative approach by working alongside staff in a local government organization, to build an evidence base to demonstrate which interventions improve children's outcomes. The process of identifying suitable programs for evaluation, thereby establishing the pre-evaluation steps, was tested using all Flying Start provision. RESULTS: The 8-Step Scoping Framework was described using the example of the local government organization Flying Start to illustrate how each step contributes to finding projects suitable for process and outcome evaluation: (1) formulating overarching key questions that encompass all programs offered by an organization, (2) gaining an in-depth understanding of the work and provision of an organization and engaging staff, (3) completing a data template per project/program offered, (4) assessing the robustness/validity of data across all programs, (5) deciding on projects suitable for evaluation and those requiring additional data, (6) negotiating with chosen project leads, both within and outside the organization, (7) developing individual project evaluation protocols, and (8) applying for ethical approval from the university and partner organization. CONCLUSIONS: This paper describes the processes involved in identifying suitable projects for evaluation. It adds to the existing literature on the assessment of specific programs suitable for evaluation and guidance for conducting evaluations by establishing the formative steps required to identify suitable programs from a range of provision. This scoping framework particularly relates to academic partners and organizations tasked with delivering evidence-based services designed to meet local needs. The steps identified have been described in the context of early years provision but can be applied to a range of community-based evaluations, or more generally, to cases where an academic partner is working with external stakeholders to identify projects suitable for academic evaluation.
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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 | 0.511 | 0.512 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.011 |
| Bibliometrics | 0.035 | 0.020 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.010 | 0.018 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".